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EpizoologicalToolsfor
AcuteHepatopancreaticNecrosisDisease
(AHPND)inThaiShrimpFarming
THESISSUBMITTEDTOTHEUNIVERSITYOFSTIRLINGFORTHE
DEGREEOFDOCTOROFPHILOSOPHY
BY
NATTAKANSALEETID
JULY2017
INSTITUTEOFAQUACULTURE
2
Declaration
3
Declaration
Iherebydeclarethat:
(1)Thisthesisiscomposedbyme,
(2)Theworkdescribedismyownwork,exceptfortheliveshrimpmovementnetwork
data from Thailand, which I analyse, but which is obtained from the Thailand
DepartmentofFisheries,
(3)ThebibliographycontainsalltheliteraturethatIhaveusedinwritingthethesis,and
(4)Theworkcontainedinthesishasnotbeensubmittedforanyotherdegree.
Signature:____________________________________________
Signatureofsupervisor:_________________________________
Date:________________________________________________
Abstract
4
Abstract
Acutehepatopancreaticnecrosisdisease(AHPND)isanemergingbacterialinfectionin
shrimpthathasbeenwidespreadacrossthemajorworldshrimpproducingcountries
since2009.AHPNDepizooticshaveresultedinahugelossofglobalshrimpproduction,
similar to that caused by white spot disease in the 1990’s. The epizootiological
understandingofthespreadofAHPNDisstillinitsearlystages,however,andmostof
thecurrentlypublishedresearchfindingsarebasedonexperimentalstudiesthatmay
struggletocapturethepotentialfordiseasetransmissionatthecountryscale.Themain
aim of this research, therefore, is to develop epizootiological tools to study AHPND
transmission between shrimp farming sites. Some tools used in this research have
alreadybeenappliedtoshrimpepizoology,butothersareusedhereforthefirsttime
toevaluatethespreadofshrimpdiseases.
AccordingtoanepizootiologicalsurveyofAHPNDinThailand(Chapter3),thefirstcase
ofAHPNDinthecountrywasineasternshrimpfarmsinJanuary2012.Thediseasewas
thentransmittedtothesouthinDecember2012.Theresultsobtainedfrominterviews,
undertakenwith143samplefarmswerestratifiedbythreefarm-scales(large,medium
andsmall)andtwolocations(eastandsouth).Boththesouthern locationand large-
scalefarmingwereassociatedwithadelayinAHPNDonsetcomparedwiththeeastern
locationandsmall-andmedium-scalefarming.
The24riskfactors(mostlyrelatedtofarmingmanagementpractices)forAHPNDwere
investigatedinacross-sectionalstudy(Chapter3).Thisallowedthedevelopmentofan
AHPNDdecision tree for defining cases (diseased farms) and controls (non-diseased
farms)becauseat the timeof the studyAHPNDwasadiseaseofunknownetiology.
Resultsofunivariateandunconditional logistic regressionmodels indicated that two
farmingmanagementpractices related to theonsetofAHPND.First, theabsenceof
pondharrowingbeforeshrimpstockingincreasedtheriskofAHPNDoccurrencewithan
odds ratio (𝑂𝑅) of 3.9 (95 % CI1.3–12.6; P-value=0.01), whereas earthen ponds
decreasedtheriskofAHPNDwithan𝑂𝑅of0.25(95%CI0.06–0.8;P-value=0.02).These
findings imply that good farming management practices, such as pond-bottom
Abstract
5
harrowing, which are a common practice of shrimp farming in earthen ponds,may
contributetoovercomingAHPNDinfectionatfarmlevel.
Forthepurposesofdiseasesurveillanceandcontrol,thestructureoftheliveshrimp
movement networkwithin Thailand (LSMN)wasmodelled,which demonstrated the
high potential for site-to-site disease spread (Chapter 4). Real network data was
recordedover a 13-month period fromMarch 2013 toMarch 2014 by the Thailand
DepartmentofFisheries.Afterdatavalidation,c.74400repeatedconnectionsbetween
13801shrimpfarmingsiteswereretained.77%ofthetotalconnectionswereinter-
province movements; the remaining connections were intra-province movements
(23%).
TheresultsdemonstratedthattheLSMNhadpropertiesthatbothaidedandhindered
diseasespread(Chapter4).Forhinderingtransmission,thecorrelationbetween𝑖𝑛and
𝑜𝑢𝑡degreeswasweaklypositive,i.e.itsuggeststhatsiteswithahighriskofcatching
diseaseposedalowriskfortransmittingthedisease(assumingsolelynetworkspread),
and the LSMN showed 𝑖𝑛-𝑜𝑢𝑡 disassortative mixing, i.e. a low preference for
connections joining sites with high 𝑖𝑛 degree linked to connections with high 𝑜𝑢𝑡
degree.However,therewerelowvaluesformeanshortestpathlengthandclustering.
Thelattercharacteristicstendtobeassociatedwiththepotentialfordiseaseepidemics.
Moreover, theLSMNdisplayedthepower-law𝑃(𝑘)~𝑘/0 inboth 𝑖𝑛and𝑜𝑢𝑡degree
distributions with the exponents (𝛾) 2.87 and 2.17, respectively. The presence of
power-lawdistributionsindicatesthatmostsitesintheLSMNhaveasmallnumberof
connections,whileafewsiteshavelargenumbersofconnections.Thesefindingsnot
onlycontributetoabetterunderstandingofdiseasespreadbetweensites,therefore,
butalsorevealtheimportanceoftargeteddiseasesurveillanceandcontrol,duetothe
detectionofscale-freepropertiesintheLSMN.
Chapter5,therefore,examinedtheeffectivenessoftargeteddiseasesurveillanceand
control inrespecttoreducingthepotentialsizeofepizootics intheLSMN.Thestudy
untilisednetworkapproachestoidentifyhigh-riskconnections,whoseremovalfromthe
networkcouldreduceepizootics.Fivedisease-controlalgorithmsweredevelopedfor
the comparison: four of these algorithms were based on centrality measures to
Abstract
6
representtargetedapproaches,withanon-targetedapproachasacontrol.Withthe
targetedapproaches,technicallyadmissiblecentralitymeasureswereconsidered:the
betweenness(thenumberofshortestpathsthatgothroughconnectionsinanetwork),
connection weight (the frequency of repeated connections between a site pair),
eigenvector(consideringthedegreecentralitiesofallneighbouringsitesconnectedto
aspecifiedsite),andsubnet-crossing(prioritisingconnectionsthatlinkstwodifferent
subnetworks).Theresultsshowedthattheestimatedepizooticsizesweresmallerwhen
an optimal targeted approachwas applied, comparedwith the random targeting of
high-riskconnections.Thisoptimaltargetedapproachcanbeusedtoprioritisetargets
inthecontextofestablishingdiseasesurveillanceandcontrolprogrammes.
Withcomplexmodesofdiseasetransmission(i.e.long-distancetransmissionlikevialive
shrimpmovement,andlocaltransmission),an𝑆𝐸𝐼𝑅𝑆compartmental,individual-based
epizootic model was constructed for AHPND (Chapter 6). The 𝑆𝐸𝐼𝑅𝑆 modelling
uncoveredtheseasonalityofAHPNDepizooticsinThailand,whichwerefoundlikelyto
occurbetweenAprilandAugust(duringthehotandrainyseasonsofThailand).Based
on two movement types, intra-province movements were a small proportion of
connections,andtheyalonecouldcauseasmallAHPNDepizootic.Themainpathway
for AHPND spread is therefore long-distance transmission and regulators need to
increasetheefficacyoftestingfordiseasesinfarmedshrimpbeforemovementsand
improvetheconductofroutinemonitoringfordiseases.Theimplementationofthese
biosecurity practices was modelled by changing the values of the long-distance
transmissionrate(𝐵6789).Themodeldemonstratedthathighlevelsofbiosecurityon
liveshrimpmovements(𝐵6789 <1)ledtoadecreaseinthepotentialsizeofepizootics
inThaishrimpfarming.Moreover,thepotentialsizeofepizooticswasalsodecreased
whenAHPNDspreadwasmodelledwithadecreasedvalueforthelocaltransmission
rate 𝐵67;<6. Hence, not only did the model predict AHPND epizootic dynamics
stochastically, but it also assessed biosecurity enhancement, allowing the design of
effectivepreventionprogrammes.
Inbrief,thisthesisdevelopstoolsforthesystematicepizootiologicalstudyofAHPND
transmissioninThaishrimpfarminganddemonstratesthat:(1)atfarmlevel,current
Abstract
7
Thaishrimpfarmingshouldenhancebiosecuritysystemseveninlargerbusinesses,(2)
atcountry level, targeteddiseasecontrolstrategiesarerequiredtoestablishdisease
surveillanceandcontrolmeasures.Althoughtheepizootiologicaltoolsusedheremainly
evaluatethespreadofAHPNDinshrimpfarmingsites,theycouldbeadaptedtoother
infectiousdiseasesorotherfarmingsectors,suchasthecurrentspreadoftilapialake
virusinNiletilapiafarms.
Acknowledgments
8
Acknowledgments
Thisthesiswouldnothavebeenpossiblewithoutthesupportofmanypeople.Firstand
foremost, Iwould liketothankmysupervisorsDarrenGreenandFrancisMurray for
providingme with the opportunity to completemy PhD thesis at the University of
Stirling.
I especially want to thank my supervisor, Darren Green, who provided me with
direction, technical support and became more of a mentor and friend than a
supervisor. He read my numerous revisions and helped make some sense of the
confusion.Iamverygratefulforhispatience,motivationandimmenseknowledgein
epidemiology.
In addition, thanks aredue tomy sponsors, theAgricultural ResearchDevelopment
Agency (Public Organization) and the Thailand Department of Fisheries for their
financial support throughout my study. My sincere thanks also go to Dr. Jiraporn
KasornchandraandDr.PutthSongsangjindaforbelievinginmeandsupportingmein
everything.
Finally,Iwishtothanktomyparentsfortheirloveandsupportthroughoutmylife, and
tomybestfriends,Paranee,ThidaratandSooksri,whoalwayshelpmeandbelievethat
Icandoit.
Tableofcontents
9
Tableofcontents
Declaration……………........................................................................................................3
Abstract…………................................................................................................................4
Listoffigures……...........................................................................................................15
Listoftables………….......................................................................................................19
Chapter1- Generalintroduction...............................................................................21
1.1 Overviewofthethesis....................................................................................21
1.2 Theimportanceandcurrentstatusoftheshrimpfarmingsector.................23
1.3 Thesupplychainoffarmedshrimp,relatedregulationsforcontrolling
productionandsocialcommunitiesofshrimpproducersinThailand......................25
1.4 ThecharacteristicsofshrimpfarminginThailand..........................................27
1.5 Infectiousdiseasesinfarmedshrimpandtheirtransmission........................30
1.5.1 Theoccurrenceofdisease.......................................................................32
1.5.2 Riskfactorsforvibriosisoutbreaksinfarmedshrimp.............................35
1.5.3 Site-to-sitetransmissionofshrimpdiseases...........................................40
1.6 Researchoutline.............................................................................................42
1.7 References......................................................................................................46
Chapter2- Epidemiologicalandepizootiologicaltoolsfordesignofdiseaseprevention
andcontrolstrategies...................................................................................................61
2.1 Experimentalstudies......................................................................................62
Tableofcontents
10
2.2 Observationalstudies.....................................................................................63
2.3 Theoreticalstudies.........................................................................................66
2.3.1 Compartmentalepidemicmodelsformicroparasitesversus
macroparasites......................................................................................................66
2.3.2 Mass-actionversusnetworkmodels.......................................................69
2.3.2.1 Mass-actionmodels.................................................................................69
2.3.2.2 Networkmodels......................................................................................70
2.3.2.3 Networkmodelsfortargeteddiseasesurveillanceandcontrol..............72
2.3.3 Individual-basedsimulationmodels........................................................75
2.4 Applicationtoacutehepatopancreaticnecrosisdisease(AHPND)................76
2.5 References......................................................................................................78
Chapter3- Evaluatingriskfactorsfortransmissionofacutehepatopancreaticnecrosis
disease(AHPND)intheThaishrimpfarmingsector.....................................................90
3.1 Abstract..........................................................................................................91
3.2 Introduction....................................................................................................91
3.3 Methods.........................................................................................................93
3.3.1 DevelopmentofacasedefinitionanddecisiontreeforAHPND.............93
3.3.2 CandidateriskfactorsforAHPNDoccurrenceatfarmlevel....................94
3.3.3 Sampledesign..........................................................................................95
3.3.4 Surveydesign...........................................................................................96
3.4 DataAnalysis...................................................................................................97
Tableofcontents
11
3.4.1 DescriptiveanalysisoftheoccurrenceofAHPNDandotherdiseases....97
3.4.2 StatisticalanalysisoftheriskfactorsforAHPND....................................97
3.5 Results..........................................................................................................100
3.5.1 IdentificationofAHPNDcasesandcontrols..........................................100
3.5.2 DescriptiveepizoologyofAHPND..........................................................104
3.5.3 RiskfactorsforAHPNDtransmissionatfarmlevel................................106
3.6 Discussion.....................................................................................................109
3.7 References....................................................................................................113
Chapter4- Analysisofthenetworkstructureoftheliveshrimpmovementsrelevant
toAHPNDepizootic.....................................................................................................119
4.1 Abstract........................................................................................................120
4.2 Introduction..................................................................................................120
4.3 Methods.......................................................................................................122
4.3.1 Datasources..........................................................................................122
4.3.2 Identificationofliveshrimpmovementtypesbyprovincialscale........123
4.3.3 Provincialvisualisationfortheliveshrimpmovementnetwork
(LSMN)……………….................................................................................................124
4.3.4 LSMNadjacencymatrixfornetworkrepresentationandanalysisatsite
level……................................................................................................................124
4.3.5 Rewiringthenetwork............................................................................129
4.4 Results..........................................................................................................132
Tableofcontents
12
4.4.1 GeneralcharacteristicsoftheliveshrimpmovementnetworkofThailand
(LSMN)……….........................................................................................................132
4.4.2 VisualisingtheLSMNbasedonnationalprovincialcentres..................137
4.4.3 Descriptiveanalysisoftheliveshrimpmovementnetwork(LSMN)atsite
level…………...........................................................................................................139
4.5 Discussion.....................................................................................................145
4.6 References....................................................................................................149
Chapter5- Target priority for targeted disease surveillance and control in the live
shrimpmovementnetworkofThailand......................................................................157
5.1 Abstract........................................................................................................158
5.2 Introduction..................................................................................................158
5.3 Materialsandmethods.................................................................................160
5.3.1 Datasourcefortheliveshrimpmovementnetwork(LSMN)................160
5.3.2 Disease-controlalgorithmsfortargeteddiseasesurveillanceand
control………….......................................................................................................161
5.3.3 Usingthedisease-controlalgorithms....................................................165
5.3.4 Characterisingthetargetedconnections..............................................166
5.4 Results..........................................................................................................166
5.4.1 Thenumberofsitesreachedinthenetwork........................................166
5.4.2 Reducingconnectedcomponentsinthenetwork.................................168
5.4.3 Thecharacteristicsoftargetedconnections.........................................171
Tableofcontents
13
5.5 Discussion.....................................................................................................172
5.6 References....................................................................................................176
Chapter6- Epizootic disease modelling in farmed shrimp using compartmental
epizooticnetwork-basedsimulations.........................................................................181
6.1 Abstract........................................................................................................182
6.2 Introduction..................................................................................................182
6.3 Materialsandmethod..................................................................................184
6.3.1 Theliveshrimpmovementnetwork(LSMN).........................................184
6.3.2 Localcontactsbetweenshrimpfarmingsites.......................................185
6.3.3 An𝑆𝐸𝐼𝑅𝑆compartmental,individual-basedepizooticmodelforacute
hepatopancreaticnecrosisdisease(AHPND).......................................................186
6.4 Results..........................................................................................................194
6.4.1 SeasonalityofAHPNDepizooticdynamics............................................194
6.4.2 Effectoflong-distanceandlocaltransmissiononAHPNDepizootic
dynamics..............................................................................................................195
6.4.3 GeographicdistributionsofAHPNDprevalenceatprovinciallevelin
Thailand……….......................................................................................................197
6.4.4 Predictiveperformanceofthe𝑆𝐸𝐼𝑅𝑆models......................................198
6.5 Discussion.....................................................................................................199
6.6 References....................................................................................................203
Chapter7- Generaldiscussion.................................................................................209
Tableofcontents
14
7.1 Summary.......................................................................................................209
7.2 Generaldiscussion........................................................................................210
7.2.1 Diseasecaseconfirmation.....................................................................210
7.2.2 Shrimpfarmingdatausedforepizoology.............................................211
7.2.3 Modellingdiseaseepizooticdynamics..................................................212
7.3 Futurework..................................................................................................213
7.3.1 Controlstrategiesforlocalnon-networkspread..................................213
7.3.2 Coinfectionepizooticmodels................................................................213
7.3.3 Geographicalinformationsystems(GIS)forshrimpfarmingsites........214
7.4 Conclusions...................................................................................................214
7.5 References....................................................................................................217
Appendices
AppendixA: Shrimpdiseasepictures 221
AppendixB: Questionsusedinbrieftelephonesurvey 223
AppendixC: Nationalprovincialcentresandabbreviation 224
Listoffigures
15
Listoffigures
Figure1.1Volumeandpercentageof shrimpproducts (rawshrimpandvalue-added
shrimp)importstotheUSAmarketbythemajorproducingcountriesin2014…............24
Figure1.2Volumeandpercentageof shrimpproducts (rawshrimpandvalue-added
shrimp)importstoEUmarketsbythemajorproducingcountriesin2014…………………..24
Figure1.3TheshrimpproductionchaininThailand…………………………………………………….25
Figure1.4OverviewofshrimpfarminginThailandfortwomajorshrimpspecies:tiger
shrimpandwhitelegshrimp,1999–2013……………………………………………………………………28
Figure1.5DistributionofThaishrimpfarmingsitesbyprovince………………………………….29
Figure1.6Theepidemiologicaltriad…………………………………………………………………………..32
Figure1.7Majorroutesinsite-to-sitetransmissionofshrimpdiseases……………………….40
Figure 1.8 Outline of the “Epizootiological tools for AHPND in Thai shrimp farming”
research…………………………………………………………………………………………………………………….45
Figure 2.1 Potential pathway for disease transmission via live shrimpmovements in
Thailand…………………………………………………………………………………………………………………….70
Figure 2.2 Epidemic network models often are often characterised by these five
simulatednetworks……………………………………………………………………………………………………71
Figure 3.1 A flow chart of the methodology used in evaluating risk factors for
transmission of acute hepatopancreatic necrosis disease (AHPND) in Thai shrimp
farming…………………………………………………………………………………………………………………….100
Figure 3.2 TheAHPNDdecision tree for determinationof higherAHPNDprobability,
lowerAHPNDprobability,andnoAHPND…………………………………………………………………102
Listoffigures
16
Figure 3.3 Report of disease status stratified according to geographic location and
farm-scalebetweenJanuary2012andMay2013……………………………………………………..105
Figure3.4ThecumulativeincidenceofAHPNDbetweenJanuary2012andMay2013,
accountingtotworegions………………………………………………………………………………………..106
Figure3.5ROCcurvesforAHPNDmodels…………………………………………………………………109
Figure 4.1 A small weighted directed network and its matrix of the shortest paths
𝐿>?……………………………………………………………………………………………………………………………127
Figure 4.2 An example of a rewiring process which generates a new network by
swappingtheendpointsoftwo-pairconnectionsinanetwork………………………………….130
Figure4.3Stronglyconnectedcomponentofadirectednetworkwitheightsites……..131
Figure 4.4 Weakly connected component of a bidirectional network with eight
sites…………………………………………………………………………………………………………………………132
Figure4.5Circa13800shrimpfarmingsiteslocatedinfiveregionsand37provincesof
Thailand…………………………………………………………………………………………………………………..133
Figure 4.6 Diagrammatic representation of LSMN demonstrating the Thai shrimp
farmingindustrystructure……………………………………………………………………………………….134
Figure4.7Distributionofthenumberofrepeatedconnectionsoverthe13-monthstudy
period(March2013–March2014)ofliveshrimpmovementsinThailand………………….135
Figure4.8Distributionofthenumberofshrimpmovedoverthe13-monthstudyperiod
(March2013–March2014)ofliveshrimpmovementsinThailand…………………………….136
Figure4.9Theprovincialstructureofthe liveshrimpmovementnetworkofThailand
(LSMN)overa13-monthperiod(March2013–March2014)……………………………………..138
Figure 4.10 The weighted degree distributions for the LSMN plotted on a log–log
scale…………………………………………………………………………………………………………………………141
Listoffigures
17
Figure 4.11 The distribution of weighted path lengths in the live shrimpmovement
networkofThailand(LSMN)isshownasafractionoftotalconnections……………………143
Figure5.1Schematicexplainingdisease-controlalgorithmswithandwithouttargeted
approachesfortargeteddiseasesurveillanceandcontrolfortheliveshrimpmovement
networkofThailand(LSMN)……………………………………………………………………………………..162
Figure 5.2 Evaluating the disease-control algorithms against the network
reachability…………………………………………………………………………………………………………….167
Figure5.3Resultsofdifferentstepsizesofthebetweennessalgorithmcomparedtothe
randomalgorithmat250removals…………………………………………………………………………..168
Figure 5.4 Evaluating the disease-control algorithms against the weakly connected
components(WCC)………………………………………………………………………………………………….170
Figure5.5Resultsofdifferentstepsizeswhencomparingthesubnet-crossingalgorithm
andrandomalgorithmat250removals…………………………………………………………………….171
Figure6.1Frequencyofnumberofsitememberspersub-district……………………………..186
Figure6.2Densityplotsof fitteddistributionsof thedata for incubationperiodsand
fallowperiods………………………………………………………………………………………………………….188
Figure6.3Designand implementationofanalgorithmforan𝑆𝐸𝐼𝑅𝑆compartmental,
individual-basedepizooticmodelforshrimpdiseaseinThailand………………………………192
Figure6.4Meannumberofinfectedsitesperseedforone-monthepizootics…………..195
Figure6.5Expectedoutcomesoftheapplicationofbiosecuritymeasuresonliveshrimp
movementsinThailand…………………………………………………………………………………………….196
Figure6.6EffectsoflargerlocalspreadinThaishrimpfarmingsectors……………………..197
Figure6.7GeographicdistributionsofAHPND-infectedprovincesinThailand…………..198
Listoffigures
18
Figure6.8ROCcurvesofthreetestmodelsidentifyingthepresenceofAHPNDinThai
shrimpfarmingsites…………………………………………………………………………………………………199
Listoftables
19
Listoftables
Table1.1Reviewsofriskfactorsforvibriosisinshrimpfarming…………………………………..38
Table2.1Threewell-knowncompartmentalmodelsandtheirapplicationto
microparasiteinfections…………………………………………………………………………………………….67
Table2.2Centralitymeasuresstudied in fivenetworksof farmedanimalmovements
resultinginoptimalstrategiesfortargeteddiseasesurveillanceandcontrol……………….74
Table3.1CandidateriskfactorsforAHPNDoccurrenceatfarmlevel…………………………..94
Table3.2CriteriausedforclassifyingThaishrimpfarmsintothreescales:small,medium
andlarge…………………………………………………………………………………………………………………...95
Table 3.3 Theoutcome from the telephone survey (Phase1) followedby face-to-face
interviews(Phase2)………………………………………………………………………………………………….103
Table3.4Cross-tabulationofoutcomesforcaseandcontrolsamples……………………….104
Table3.5ThestatisticallysignificantriskfactorsforAHPNDwithoddsratios(𝑂𝑅s)and
95%confidenceintervals…………………………………………………………………………………………107
Table3.6UnconditionallogisticregressionanalysisofriskfactorsforAHPND……………….108
Table3.7Cross-validationresultsontheAHPNDmodelsobtainedfromunconditional
logisticregression…………………………………………………………………………………………………….108
Table 4.1 Degree properties of the live shrimp movement network of Thailand
(LSMN)………………………………………………………………………………………………………………….…140
Table4.2Descriptionofthenumberofconnectionsbetweenseed-producingsitesand
ongrowingsitesbasedontheweighteddegreeoftheLSMN…………………………………….142
Table4.3Descriptionofthenumberofconnectionsbetweenseed-producingsitesand
ongrowingsitesbasedonthenon-weighteddegreeoftheLSMN……………………………..142
Listoftables
20
Table 4.4 Estimated maximum and mean reach, size of giant strongly connected
components(GSCCs),andsizeofgiantweaklyconnectedcomponent(GWCCs)forboth
theLSMNandtherewiredLSMNs…………………………………………………………………………….145
Table 5.1 Source and destination site types of the top 1 000 removals from the
betweenness-based algorithm shown by probabilities (in percentages) in the total
numberofremovals,andinthewholeconnections………………………………………………….172
Table5.2Sourceanddestinationsitetypesofthetop1000removalsfromthesubnet-
crossingbasedalgorithmshownbyprobabilities(inpercentages)inthetotalnumberof
removals,andinthewholeconnections…………………………………………………………………..172
Table6.1Akaike'sInformationCriterion(AIC)valuesoffitteddistributions………………188
Table 6.2 Real pattern of AHPND epizooticswithin shrimp farming sites of Thailand
reportedinJuly2013………………………………………………………………………………………………..193
Chapter1Generalintroduction
21
Chapter 1 - General introduction
1.1 Overviewofthethesis
Farmedpenaeidshrimparethehighestvaluespeciesinworldaquacultureproduction
atapproximatelyUSD22000millionin2014(FAO,2016b).Shrimpisthemostimportant
internationally tradedfisherycommodity inboththeUnitedStatesofAmerica (USA)
and the EuropeanUnion (EU)markets (FAO, 2016a). Further, shrimp farming drives
economic growth for many countries, provides a source of income and better
livelihoodsforproducers,anddevelopsmanyrelatedbusinessesinthewholeshrimp
industry.
Thailand isoneof the top shrimp-producing countries (FAO,2016b),withanannual
productionofaround500000–600000 tonnesbasedon2014 figures (Undercurrent
News,2014),andwith85%of this total soldoutside thecountry (Alam,2015).The
shrimpsupplychain inThailandcomprisesofhatcheries,ongrowing farmers, traders
and brokers, shrimp auction markets and processing plants (Alam, 2015). Farmed
shrimprepresentsoneofthemajoragriculturalproductsdrivingthegrowthinannual
Thai gross domestic product (GDP) from 2.9 % in 2015 to 3.2 % in 2016 (National
Economic and Social Development Board, 2017). Moreover, about 30 % of Thai
labourers are in the agriculture sector (TheWorld Bank, 2015). The shrimp farming
sector is therefore a key element in allowing exporting countries like Thailand to
improvetheirsocialandeconomiccircumstances.
Nevertheless, the growthand sustainabilityof shrimp farming is affectedbydisease
outbreaks, mainly caused by microparasites such as viruses, bacteria, fungi and
protozoans.Thitamadeeetal.(2016)describerecentdiseasesthreateningAsianshrimp
farming,thebiggestsourceofshrimpworldwide.They indicatethatanewemerging
disease,acutehepatopancreaticnecrosisdisease(AHPND)isofmostconcern,together
withthereoccurrenceofviraldiseasessuchaswhitespotsyndromevirusandyellow
headvirus(Thitamadeeetal.,2016).
Importantly, thewidespreadpresenceofdiseases leads to theuseof chemicals and
antibioticsinfarming(Chenetal.,2015;Holmströmetal.,2003;Ricoetal.,2012;Uchida
Chapter1Generalintroduction
22
etal.,2016).Theresiduesofthesechemicalsandantibioticsnotonlyhaveapotentially
adverse effect on human health such as causing the development of antibiotic
resistance(Rochaetal.,2016)andsomeareactuallytoxic(Somjetlerdcharoen,2002),
buttheyalsoleadtointernationaltradedisputes(FDA,2016)anddramaticpollutionof
theenvironment(LeandMunekage,2004).Thesenegativeimpactsofchemicalsand
antibioticsonhumanhealth,tradeandtheenvironmentinferthatdiseaseprevention
and controls (e.g. biosecurity, good farm management and disease surveillance
measures)arethebestmanagementinterventionsforshrimpfarming(Brugereetal.,
2017;ChinabutandPuttinaowarat,2005).
Intermsofdiseasepreventionandcontrols,in1998theThaiauthoritieslaunchedthe
NationalDiseaseSurveillanceandMonitoringProgrammeforShrimpFarming(NACA,
2017).Furthermore,themovementsofshrimpbetweensourcesitesanddestination
sites,themostcommonpathwayforsite-to-sitediseasetransmission,arecontrolledby
the aquatic animal trade regulation of Thailand, B.E.2553 (2010). Authorised users
recordreal-lifeliveshrimpmovementsinacomputersystem;datathatisreferredtoin
thisresearchastheLiveShrimpMovementNetworkorLSMN.Althoughtheresultsof
networkmodellingprovideagooddescriptionofdiseasespreadandareutilisedinmost
control programmes (Green et al., 2012; Keeling and Eames, 2005;Werkman et al.,
2011),thedatafromtheLSMNhasneverbeenappliedinnetworkmodellingtoexamine
diseasespread.
In order to protect the Thai shrimp farming from AHPND and other diseases,
epizootiological studies are needed. Four epizootiological questions are therefore
analysed in this research using a variety of tools. The investigated epizootiological
questionsconsistof:
(1)WhataretheriskfactorsforthespreadofAHPNDatfarmlevel?(Chapter3);
(2)Howdoes thenetwork structureof live shrimpmovements influence site-to-site
diseasetransmission?(Chapter4);
(3) How can we identify those live shrimp movements at high risk of disease
transmissionfromsitetosite?(Chapter5);and
Chapter1Generalintroduction
23
(4)WhatistheoverallAHPNDprevalencewhenthediseaseiswidespreadthroughboth
long-distanceandlocaltransmission?(Chapter6)
These research outcomes can be used to improve existing disease prevention and
controlmeasuresandregulations, i.e. inrespecttobiosecurity,certificationschemes
forshrimpfarminganddiseasesurveillanceandcontrolprogrammes.Thebackground
ofshrimpfarmingisdescribedinmoredetailinthenextsectioninordertojustifythe
importanceofthisresearch.
1.2 Theimportanceandcurrentstatusoftheshrimpfarmingsector
Giventhattheworldpopulationisexpectedtoriseto12.3billionin2100(Gerlandet
al., 2014), the aquaculture sector has a high potential to produce large amounts of
humanfoodcomparedwiththefisherysector.Ascanbeseeninthestatisticalreportof
theFoodandAgricultureOrganizationoftheUnitedNations(FAO,2016b),thereisa
clearcontrastbetweenaquacultureandcapturetrendsoverthe29yearsfrom1985to
2014: aquaculture productionhas increased gradually from10 to 70million tonnes,
whileproductionfromfishcapturehasremainedstableatc.80–90milliontonnes.
Aquacultureoffersvariousfoodcommodities: fish,crustaceansandmolluscs.Among
the product varieties, penaeid shrimp (tiger shrimpPenaeusmonodon andwhiteleg
shrimpLitopenaeusvannamei)aretwoofthedominantspeciesforinternationaltrade.
They play an important role in food consumption, and drive economic growth and
enhancepeople’slivelihoodinmanyagriculturalcountries(FAO,2016b).Thecountries
shown in Figures 1.1 and 1.2, respectively,were themajor shrimp exporters to the
UnitedStatesofAmerica(USA)andtheEuropeanUnion(EU)in2014(FAO,2015).
Moreover,farmedshrimpisahigh-valueproduct.InJanuary2017,shrimpprices(per
kg)weretwotimesmoreexpensivethanPangasiussp.,acommercialfreshwaterfish
(FAO,2017).Thehighlevelsofincomethatitispossibletomakefromshrimpmeans
that shrimp farming has become widespread (Filose, 1995). The net income, for
example,ofsmall-scaleintensivefarminginIndiawasaround2000USDperhectare
and 9 000 USD per hectare for medium-scale intensive farming (Bhattacharya and
Ninan,2011).
Chapter1Generalintroduction
24
These show the importance of shrimp farming for global food supply and socio-
economicstatus.Whendiseaseiswidespread,therefore,thehugeeconomiclossisthe
obviousoutcomethroughoutthesupplychainoffarmedshrimp.
Figure 1.1 Volume and percentage of shrimp products (raw shrimp and value-added shrimp) imports to the USA market by the major producing countries in 2014 (unit: thousand tonnes). The total shrimp imported into USA was around 570 thousand tonnes. Thailand was the fifth-largest supplier (FAO, 2015).
Figure 1.2 Volume and percentage of shrimp products (raw shrimp and value-added shrimp) imports to EU markets by the major producing countries in 2014 (unit: thousand tonnes). The total shrimp imported into the EU was around 790 thousand tonnes. Thailand was the 14th-largest supplier (FAO, 2015).
6.7
8
11.8
17.9
20.2
32.5
64.6
73.8
92.5
103.4
108.8
0 20 40 60 80 100 120
Guyana
Honduras
Peru
Malaysia
Mexico
China
Thailand
Vietnam
Ecuador
Indonesia
India
Thousandtonnes
(5)Thailand
Coun
tries
(19.1%)
(18.2%)
(16.3%)
(13%)
(11.3%)
(5.7%)
(3.5%)
(3.1%)
(2.1%)
(1.4%)
(1.2%)
15.618.218.8
22.725
28.835.535.8
40.744
49.755.1
66.283.2
93.1
0 20 40 60 80 100 120
IndonesiaThailandGermanyBelgium
SpainChina
NetherlandsCanada
BangladeshDenmarkVietnam
GreenlandArgentina
IndiaEcuador
Thousandtonnes
Coun
tries
(14)Thailand
(11.8%)(10.5%)
(8.4%)(7%)
(6.3%)(5.6%)(5.1%)
(4.5%)(4.5%)
(3.6%)(3.2%)(2.9%)
(2.4%)
(2%)(2.3%)
Chapter1Generalintroduction
25
1.3 Thesupplychainoffarmedshrimp,relatedregulationsforcontrollingproductionandsocialcommunitiesofshrimpproducersinThailand
ThissectionoutlinesthesupplychainofThaishrimpproduction.ItalsodescribesThai
regulations(i.e.movementcontrols,sitecertificationandfarmingregistration),andthe
socialcommunitiesofshrimpproducersinThailandthathavebeenincorporatedinto
ourresearch.
The supply chain for farmed shrimp in Thailand is simple (Figure 1.3). For hatchery
productionofshrimpseed,thewildbroodstockofPenaeusmonodoniseithercaptured
from the sea or cultured in a breeding programme, whereas using broodstock of
Litopenaeusvannameifromabreedingprogrammeisacommonpractice(Lebeletal.,
2010).Alam(2015)andUddin(2008)demonstratethattheshrimpindustryinThailand
entailsthreestepsforpassingtheproductbetweenahatcherysiteanddomesticand
globalmarkets.Inthisresearch,however(Chapters4,5and6),thenetworkmodelling
ofdiseaseepizooticshasbeen focusedonthe transmissionbetweenseed-producing
sites (hatcheries and nurseries) and ongrowing sites, a process that denotes a large
numberofliveshrimpmovementsforfarming.Theremainingsteps(i.e.movementsof
chilledorfrozenshrimpfromongrowingsitestotraders,brokers,processingplantsor
auctionmarkets)shouldnotposeariskofspreadingtoshrimpfarmingsites.
Figure 1.3 The shrimp production chain in Thailand (modified from Alam, 2015 page 103). The live shrimp movement data used in the research demonstrate the movements of live shrimp (shrimp seed) between hatchery, nursery and ongrowing sites, as shown in the box.
Hatcherysite
Nurserysite
Ongrowing site
Trader/broker
Processingplant
Auctionmarket
Domesticmarket
Globalmarket
Broodstock
Seed
Sea
Seed
Seed
Chapter1Generalintroduction
26
Uddin (2008) indicates that thesupplychain for farmedshrimpstarts fromhatchery
sites,whichproduceshrimpseedforongrowingsites. Insteadofdirectsellingtothe
ongrowing sites, hatchery sites pass some of their production to nursery sites at
nauplius stage or initial postlarval stage. Then, nursery sites rear the seed from the
naupliusuntilthepostlarval(PL)stage(mostlyPL10;rearedfor20days)beforeselling
theproductiontotheongrowingsites(FAO,2014).Commonly,theproductionperiod
for whiteleg shrimp (L. vannamei) is 105–120 days at pond level with a density at
400000–500000shrimpperhectare,obtainingaharvestsizeof21–25gforprocessing
plants(Wyban,2007).Someproducerspracticeahigherdensitystockingof900000–
1200000shrimpperhectareforatargetedsizeof12–18gincaseofpartialharvest,
and24gforfinalharvest.Currently,shrimpfarmingcangenerateaproductioncapacity
of twoor threecyclesperyear (Limsuwan,2009).Shrimpfarmingwithaproduction
capacityofthreecyclesperyeartendstohaveahighriskofdiseasesbecauseafallow
periodtotreatanddisinfectpathogensisshorterthanthatoffarmingoneortwocycles
peryear(Cocketal.,2009;Muniesaetal.,2015).
The movements of live shrimp in each of the steps mentioned above are closely
recordedintheliveshrimpmovementrecord(KongkeoandDavy,2010;Yamprayoon
andSukhumparnich,2010).Thisrecordfollowstheaquaticanimaltraderegulationof
Thailand,B.E.2553 (2010).All producersmust inform theproperauthorities (i.e. the
ThailandDepartmentofFisheriesstaffandtheirrepresentatives)aboutthemovements
of shrimp. Insteadof apaper-based system to collect the shrimpmovementdata, a
computer-basedsystemhasbeenusedbyThaiauthoritiessinceMarch2013andsuch
electronic records subsequently are printed on a paper for checking (Songsanjinda,
2013).Moreover,allshrimpfarmingsitesmustberegisteredlegallyandtheirfarming
management practices should be inspected under the governmental certification
schemes(KongkeoandDavy,2010).Farmingstandardsandcertificationaredeveloped
toenhance foodsafetyonaquacultureandsustainability includingenvironmentand
livelihoodreasons(Corsinetal.,2007;Piumsombunetal.,2005;Pongthanapanichand
Roth,2006;YamprayoonandSukhumparnich,2010).Thecriteriaforfarmingstandards
includegoodhealthmanagementof farmedshrimp,diseasepreventionandcontrol,
Chapter1Generalintroduction
27
andtheapplicationofmovementdocumentsfortraceabilityintheshrimpproduction
chain(NationalBureauofAgriculturalCommodityandFoodStandards,2014).
As a regulatory requirement for the control of shrimp production, the live shrimp
movementrecordinThailandprovidesnewandusefuldataforepizootiologicalstudies.
Thisofficialrecordof liveshrimpmovementscanhelptoindicatepotentialroutesof
infectiousdiseasetransmissionfromsitetosite,andsince2013thesedataaremore
readilyobtainedandanalysed.TheresearchpresentedinChapters4,5and6isthefirst
studytousethisdatasourceforshrimpepizoology,however.Moreover,priortothis
thesis,thereisnoevidencethattheliveshrimpmovementdatahasbeenutilisedasa
partofdiseasepreventionandcontrolintheThaifarmingcertificationscheme(i.e.for
farmmonitoringprogramme).
Inaddition,socialcommunitiesofshrimpproducers,i.e.shrimpfarmerclubs,havean
influence on disease prevention and controls. Shrimp farmer clubs support better
farmingpractices(Kassametal.,2011),contributingtoadecreaseofdiseaseoutbreaks
(KongkeoandDavy,2010).Importantly,theannualconferencesarrangedbytheseclubs
generateanexchangeoftheideasbetweenproducersandhelptoimproveknowledge
aboutfarmpracticesandshrimphealthmanagement.WhenthecausalagentofAHPND
remainedunknown, the Thai shrimp farmer clubsparticipated in settingup suitable
broodstock feedingpractices inhatcheries. Shrimp fry frombroodstock treatedwith
non-livefeedsbecameakeyagreementbetweenshrimpsellersandbuyers(Suratthani
Shrimp Farmers Club, 2014). Consequently, the role of polychaete worms, bivalve
molluscs and other live feeds in disease transmission to farmed shrimp could be
decreased.Hence,socialfarmingcommunitieshaveparticipatedintheeffectivenessof
diseasecontrolstrategiesinThailand.
1.4 ThecharacteristicsofshrimpfarminginThailand
Figure 1.4 shows the approximately 20 000 shrimp farming sites in Thailand that
together generate up to 600 000 tonnes of shrimp production annually (2011).
Productiondecreasedsubstantiallyin2013,however,mainlyduetodiseaseproblems
(FAO,2013a).ThefigurealsoillustratesaneweraintheThaishrimpindustryin2003,
Chapter1Generalintroduction
28
whenwhitelegshrimpwereintroducedtotheThailandin2003.Theintroductionofnew
shrimpspecies,togetherwiththedevelopmentofnewtechnologiesandinnovations,
ledtoalargeincreaseintotalproductionwithasmallernumberofshrimpfarmingsites.
Thesesitesadoptmoreintensivesystems,whichoftendeveloppoorwaterqualityand
stressfulconditioninfarmedshrimp(Kautskyetal.,2000).
Figure 1.4 Overview of shrimp farming in Thailand for two major shrimp species: tiger shrimp and whiteleg shrimp, 1999–2013. The left axis of the graph presents the number of ongrowing sites. The right axis shows the yield (in tonnes) of farmed shrimp production (modified from Thailand DoF, 2016).
Regarding the geographic location of shrimp farming sites in Thailand (Figure 1.5),
Thailandhasanapproximately2600km-longshorelinealongtheGulfofThailandand
theAndamanSea(Tookwinasetal.,2005),withalargenumberofshrimpfarmingsites
are intensively established along these coastal areas. The remainder, called inland
farmingsites,aresituatedawayfromtheshoreline,drawingwaterfromrivers,canals
orlakes.Notonlyarethesewaterbodiesthesourceofwaterforfarming,buttheyare
alsousedfordischargeofnutrientsand,concomitantly,pathogensduringwater-pond
discharge (Barraza-Guardado etal., 2013;Marchand etal., 2014). Thismeans thata
groupofneighbouringsitesthatsharenaturalresourceshaveasharedriskofdisease
transferthroughhydrologicalconnectivity.
0
200000
400000
600000
800000
0
10000
20000
30000
40000
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013Years
40 000
30 000
20 000
10 000
800 000
600 000
400 000
200 000
00
Numbe
rofo
ngrowings
ites
Farm
edsh
rimpyield(to
nne)
Whitelegshrimp(2003)
AHPND(late2011)
Totalshrimpyield(tonne)Tigershrimpyield(tonne)Whiteleg shrimpyield(tonne)
Chapter1Generalintroduction
29
Figure 1.5 Distribution of Thai shrimp farming sites by province. Data summarised from the live shrimp movement data between March 2013 and March 2014 and figure illustrated using the tmap package, in the 𝑹 Programme Environment (R foundation for statistical computing, 2015). The data were also used to construct network models in Chapters 4, 5 and 6.
The different geographic locations of shrimp farming sitesmean that variouswater
salinitylevelsareusedforrearingshrimp.Ahigh-salinitysystem,withasalinityrange
between10–30ppt,isappliedtoshrimpfarminglocatednearthecoast,whereasthe
inlandfarmingoperatesunderalow-salinitysystemof3–4ppt(Flahertyetal.,2000;
Tookwinas et al., 2005). Importantly, low salinity farming causes adecreased innate
immuneabilityinshrimpandalsoreducestheirresistancetobacterialdiseases(Wang
andChen,2006;WangandChen,2005).Thesalinityparameterisonlyoneofanumber
ofenvironmentalfactorsthataffectsusceptibilitytoinfectioninshrimpfarmingsites.
The occurrence of diseases, however, is dependent on the nature of the disease
(microparasiticormacroparasitic)anditstransmission,whichcanbeexplainedinterms
oftheepidemiologicaltriad(Section1.5.1).
GulfofThailand
AndamanSea ShrimpfarmingindexNon-shrimp farmingprovinces
0to500sites500to1000sites1000to1500sites1500to2000sites
Chapter1Generalintroduction
30
1.5 Infectiousdiseasesinfarmedshrimpandtheirtransmission
MicroparasiteshavebeenamajorcauseofseverediseaseoutbreaksinAsianshrimp
farmingoverthepast30years(Flegel,2012).Sixtypercentof lossesareduetoviral
diseases,20%duetobacterialdiseases(particularlyvibriosisasdescribedinDisease
box1),andtheremaininglossesduetootherparasites(Flegel,2012).Vaccinationand
immunostimulants are a challenge for inhibiting disease spread in shrimp farming
(Johnson et al., 2008; Namikoshi et al., 2004). An example of successful vaccines is
provided on vaccination trials with P. monodon to induce resistance to white spot
syndromevirus(Vaseeharanetal.,2006). Nevertheless,thereisevidencethattheiruse
rarely succeeds in general farming for two reasons: the lackof anadaptive immune
systeminshrimp,andthepresenceofmultiplepathogenswithinsites(Camposetal.,
2014;Cocketal.,2009;Gräslundetal.,2003;GräslundandBengtsson,2001;Supungul
etal.,2015).Epizootiologicalstudiestodescribetheoccurrenceofdiseasesandtheir
transmissionarethereforecurrentlyrequiredintheshrimpfarmingindustry.
Toincreaseunderstandingofthedynamicsofdiseaseepizooticsinshrimpfarming,the
natureofmajorshrimpdiseases,i.e.vibriosis,whitespotdisease,yellowheaddisease,
infectioushypodermalandhaematopoieticnecrosis,acutehepatopancreaticnecrosis
disease and taura syndrome, together with their routes of transmission, are briefly
outlinedinthissection(Diseaseboxes1–6andAppendixA).Importantly,inChapter3
“Evaluating risk factors for transmission of acute hepatopancreatic necrosis disease
(AHPND) in the Thai shrimp farming sector”, the visible clinical signsof thesemajor
shrimpdiseaseshavebeenusedinthedifferentiationoftheAHPND-affectedsitesfrom
sitesaffectedbyotherdiseases,andthusitisimportanttoexplainthoseclinicalsigns
here.
Chapter1Generalintroduction
31
Diseasebox1:Vibriosis
Vibrio species that commonly cause vibriosis in farmed shrimp are, for example,
V.alginolyticus,V.harveyi,V.parahaemolyticusandV.penaeicida(Saulnieretal.,2000).
ClinicalsignsofinfectedshrimpvarywiththetypeofVibriospp.Infectedshrimpcanhave
paleandopaquemusclesandblackstripesonthelateralcephalothoraxpresentininfected
shrimp(Longyantetal.,2008).Abright-redsyndromeiscausedbyV.harveyi,wherethe
infected shrimp shows red discoloration spots on the abdomen (Soto-Rodriguez et al.,
2010).ShrimpinfectedwithV.choleraeshowanexteriorvisualappearanceoflegyellowing
(Caoetal.,2015).Vibriosisleadstolowsurvivalratesinhatcheriesandongrowingsites.In
many cases, outbreaks of vibriosis have caused mass mortality among small shrimp in
hatcherysites,suchasinTaiwanin1994(Liuetal.,1996),andChinain1995(Vandenberghe
et al., 1998). Vibriosis also largely occurred in farmed shrimp in Sri Lanka in 2010
(HeenatigalaandFernando,2016).
Diseasebox2:Whitespotdisease
Whitespotdisease(WSD)isaninfectionofshrimpbywhitespotsyndromevirusorWSSV
(OIE,2013b).WSDwasfirstreportedinTaiwanin1992(Chouetal.,1995).WSDspreadhas
amongmanyoftheshrimpproducingcountries inAsia (e.g.China, Japan,Korea,Thailand,
IndiaandBangladesh)withinoneortwoyearsofitsfirstdetection(Escobedo-Bonillaetal.,
2008),andinNorthAmericain1995(Lightner,1999).MostshrimpinfectedwithWSSVexhibit
white spots on their exoskeleton and lesions on the cephalotholax (Cheng et al., 2013;
Rodríguez et al., 2003), however poor water quality such as high alkalinity or bacterial
diseasemayalsocausethesewhitespots(OIE,2013b).WSDinfectionresultsinhighmortality
infarmedshrimp,upto100%withinoneortwoweeks(Rajendranetal.,1999;Wuetal.,
2005).
Chapter1Generalintroduction
32
Diseasebox3:Yellowheaddisease
Yellowheaddisease(YHD)isaninfectionofshrimpbyyellowheadvirusorYHV(OIE,2013b).
The first YHD epizootic was identified in Asia in 1990 (Walker and Winton, 2010). The
economic losses from YHD outbreaks were reported at an estimated USD 3 million in
Thailand(2008),forexample(Senapinetal.,2010).Grosssignsofdiseasedshrimpinclude
yellowishcolourationandswollencephalothorax(Lio-Poetal.,2001).Cumulativemortality
of 60–70%has been reported amongP.monodon and L. vannamei cultured in earthen
ponds(Senapinetal.,2010).
1.5.1 Theoccurrenceofdisease
Anunderstandingofdiseaseoccurrenceisimportanttoshrimpepizoology.Alldiseases
aremultifactorial,asmanifestedbytheepidemiologicaltriadmodel(Figure1.6).The
diseasesexistwhenthereisinteractionbetweenpathogen,hostandtheenvironment.
Figure 1.6 The epidemiological triad (from Rockett, 1999 page 10).
Pathogens, in termsofmicroparasites suchasvirusesandbacteria, causenumerous
infectionsinshrimpfarming,despitethefactthatmanycontrolmeasurestoprevent
microparasitic infectionshavebeendevelopedand implemented.Microparasitesare
distinguished from macroparasites by their small size, the short time required to
completetheirgeneration,andtheirhighability fordirect reproductionwithinhosts
(Anderson and May, 1981). Thus, the incidence and prevalence of disease due to
microparasites often tends to be high, particularly in farming conditions like shrimp
rearing.Oneoftheimportantmeasurestopreventmicroparasiticinfectionsinshrimp
EnvironmentHost
Pathogen
Chapter1Generalintroduction
33
farming is the use of shrimp seed produced under specific pathogen free (SPF)
conditions(Lightneretal.,2009;Mossetal.,2012).Thismeasure,however,maynotbe
sufficientduetothecomplexityinthelifecyclesofpathogens.Forexample,ifWSSVis
latent,WSD-infectedshrimpmaynotbedetectedwithacommercialiseddiagnostictest
(HeandKwang,2008;Khadijahetal.,2003).Inaddition,inSPFconditions,shrimpare
only free from specifically targeted pathogens, and thus remain at risk from non-
targetedorunknownpathogen(Barmanetal.,2012).Hence,stockingSPFshrimpisjust
onemeasureforpreventingandcontrollingdiseaseoccurrenceinshrimpfarming.The
pathogenmaynotbedetectedinallcasesofSPFshrimp,andsuchshrimpfarmingsites
canbeinfectedviaotherpathways.
Shrimparehostsofdiseasesinthisthesis.Shrimparesusceptibletoawidevarietyof
pathogens,andespeciallytoviruses(Lightner,2011;Thitamadeeetal.,2016).Shrimp
susceptibility to a particular disease is affected by the species concerned (Bell and
Lightner, 1984; Lightner et al., 1998;Overstreet et al., 1997), tolerance to infection
(Hameedetal.,2000;Witteveldtetal.,2006),andlifehistorystage(Aguirre-Guzmánet
al., 2001; Sudha et al., 1998). Many severe viral outbreaks in shrimp are due to
persistent infections at a low level (Walker andWinton, 2010). Shrimp can also be
infectedbymultiplepathogenssuchasbyhepatopancreaticparvovirusandmonodon
baculovirus(Flegeletal.,2004).Thisevidencedemonstrateskeyconditionsofdisease
occurrence,although,inreality,avarietyofenvironmentalfactorsinfluencethehealth
offarmedshrimp.
Themajorenvironmentalfactorsaffectingshrimpfarminghavebeenevaluatedfortheir
association with diseases. Environmental factors that affect shrimp health include
climate (Piamsomboon et al., 2016), seasonality (Boonyawiwat, 2009) and storms
(Zhangetal.,2016). TheexperimentsinRahmanetal.(2006)andRahmanetal.(2007)
showedthatahighwatertemperatureof33°Cwasrelateddirectlytoareductionin
shrimpmortalityfromWSD(Diseasebox2),comparedwithatemperatureof27°C.The
lowest(0.5%)andhighestsalinitylevels(5.4%)wereassociatedwithhighWSD-related
mortalityinshrimp(Ramos-Carreñoetal.,2014).Environmentalfactorsmayalsotrigger
diseasereoccurrence,suchas inthecaseof infectionwith IHHN(Diseasebox4)and
AHPND(Diseasebox5).AshrimppondinfectedwithIHHNhasaprobabilityofrepeated
Chapter1Generalintroduction
34
IHHNonsetwhenthetemperaturewasbelow24°C(Montgomery-Brocketal.,2007).In
addition,ahighpHlevel,at8.5–8.8,wasidentifiedasassociatedwithrepeatedAHPND
onsetinaffectedshrimppondsinMalaysia(AkazawaandEguchi,2013).Thisevidence
demonstrates that the environment affects the susceptibility to disease of farmed
shrimp.
Diseasebox4:Infectioushypodermalandhaematopoieticnecrosis
Infectious hypodermal and haematopoietic necrosis (IHHN) is infection of shrimp by
infectioushypodermalandhaematopoieticnecrosisvirus(OIE,2013b).Thefirstoccurrence
of IHHNinfarmedshrimpwasobservedinHawaii inthe1980’sduetothemovementof
IHHN-infectedpostlarvaefromCentralorSouthAmerica(Lightneretal.,1983a).Thecuticle
of the shrimp is found to be whitish in diseased shrimp and, generally, therewas high
mortality (> 80%)after shrimpmoulting (Lightner et al., 1983b). IHHN-infected shrimp
often exhibit runt-deformity syndrome, resulting in a reduced growth rate, and a high
presenceofrostrum,antennaeorcuticledeformity(Chayaburakuletal.,2005;Kalagayanet
al.,1991).
Diseasebox5:Acutehepatopancreaticnecrosisdisease
Acute hepatopancreatic necrosis disease (AHPND) occurs when shrimp are infected by
specificstrainsofVibrioparahaemolyticusbacteria(Tranetal.,2013).Further,thebacteria
produceatoxinthatdamagesthehepatopancreasofshrimp(Laietal.,2015).AHPNDhas
beeninvadingmajorshrimpproducingareas:China(2009),Vietnam(2010),Malaysia(2011),
Thailand (2011/2012),Mexico (2013),and,most recently, thePhilippines (2015). Itcauses
high mortalities, up to 100 % within 35 days post stocking (Eduardo and Mohan, 2012;
Kasornchandra, 2014). Gross signs can be seen in ongrowing ponds, i.e. empty gut and
stomach,andpaleandatrophiedhepatopancreasofaffectedshrimp(NACA,2014).
Chapter1Generalintroduction
35
1.5.2 Riskfactorsforvibriosisoutbreaksinfarmedshrimp
The link between evidence for vibriosis in varied conditions and the actual field
conditionsandfarmingpracticesthat leadstoAHPNDarereviewedhere(Table1.1).
The geographic location of the farm has been suggested as an important factor in
increasingtheproductivityofshrimpfarmingbutalsointhesusceptibilitytoinfection
of shrimp farming sites (Zhu and Dong, 2013). Some examples of the relationship
betweenthelocationoffarmingsitesandthevibriosisaregiveninTable1.1(part1).
Gopaletal.(2005)foundthatthenumbersofVibrioinshrimpfarmingofinIndiawere
higheralongthewestcoastthanthesouthcoast,ataround102cfupermlwater.The
establishmentoffarmsnearhumancommunitiesalsoincreasedtheriskofvibriosisdue
to largeamountsofheavyorganicmaterial fromthehumancommunityflowing into
naturalsources(Mohneyetal.,1994;ReillyandTwiddy,1992).Farmslocatedcloseto
estuaries commonly facedwidely fluctuating salinity levels,withhigh fluctuationsof
salinity from 35 % to 5 and 15 % being associated with an increased risk of
V. alginolyticus infection in farmed whiteleg shrimp (Wang and Chen, 2005). Farms
established in, or close to, agriculture areas risked contamination from methyl
parathion (pesticides) that led to increased susceptibility to V. parahaemolyticus
infectioninshrimp(Roqueetal.,2005).
Climateappearstoinfluenceoutbreaksofvibriosisamongshrimpfarms.Thewetseason
hasbeenrelatedtothegrowthofV.cholerae(ReillyandTwiddy,1992),whilecooler
temperatures,at20°C,havebeenrelatedtoanincreasedriskofV.penaeicidaamong
cultured blue shrimp P. stylirostris (Goarant et al., 2000); in turn, warmer water
(changedfrom27to32°C)hasbeenrelatedtoanincreasedriskofV.alginolyticusfor
culturedL.vannamei(Chengetal.,2005).
Themostimportantfactorsthatappeartoinfluencevibriosisoutbreaks,however,are
inappropriate farming management practices. Shrimp fed untreated Artemia risked
V. parahaemolyticus and V. harveyi infections (Quiroz-Guzmán et al., 2013). High
phytoplankton dynamics incurred an abundance of Vibrio spp. within shrimp ponds
(Lemonnier et al., 2016). Tho et al. (2012) demonstrated that sediment provided a
bettermicroenvironment for Vibrio thanwater and,particularly in the rainy season,
Chapter1Generalintroduction
36
pondsedimentprovidedthebesthabitatforV.nigripulchritudo(Goarantetal.,2006;
Wallingetal.,2010)andV.cholerae(Lekshmyetal.,2014).Asanattempttodecrease
thelargeamountofpondsediment,therefore,generallyearthenpondshavebeenlined
withplasticsheets.
A linedpond is considered tobebetterpractice in termsof reducing thevolumeof
sediment.Thisisatechniquethatisgenerallyapplicabletointensiveshrimpfarming.
ReillyandTwiddy(1992),however,havedemonstratedthatintensivefarmingsystems
increasedtheriskofV.cholerae infectioninfarmedshrimp.Rearinghighnumbersof
shrimpwithinthelinedpondsledtoahighnutrientconcentrationinthewaterpond,
whichisimportantasariskfactorforvibriosis(Funge-SmithandBriggs,1998).Thathigh
abundance of V. choleraandV. parahaemolyticuswas caused by the heavy organic
matterwithinshrimppondswasproposedbyGaneshetal.(2010).HighpHlevels(>7),
highsalinity(>0.5%)andhighammoniaarealsorelatedtoVibrioabundanceinshrimp
ponds (Heenatigala and Fernando, 2016; Lekshmy et al., 2014; Liu and Chen, 2004;
LokkhumlueandPrakitchaiwattana,2013).AhighriskofVibriodiseaseswasdetectedwhenever shrimp ponds lacked diversity of Vibrio communities (Sung et al., 1999).
Comparedwithlinedponds,earthenpondscouldbefullypreparedbydrying,harrowing
andfillingwithprobioticBacillus,toreducetheriskofVibrio(Moriarty,1998;Nimratet
al.,2008).
Vibriosisisawaterbornediseaseandextractionofculturewaterfromtheseahasbeen
identified as a risk factor in terms of increasing the presence of V. harveyi and
V. splendidus (Lavilla-Pitogo et al., 1990), demonstrating the importance of water
treatmentat thebeginningof stocking.Although farmsoftenuse recirculatedwater
systems(meaningnoextractionofculturewaterfromthesea),Vibriocouldstillgrow
rapidly,principallyduetothepoorqualityofthewaterreusedinthefarms(Colt,2006).
Thisispossiblyrelatedtothepoorplanningoftherecirculatingsystems,whichaidsthe
transmissionofdiseasesfrominfectedpondstoothers(Funge-SmithandBriggs,1998;
Mugnieretal.,2013).
Vibriosisinshrimpfarmingoftenco-occurswithotherpathogens.Whenfarmedshrimp
areinfectedbyWSD,Kannapiranetal.(2009)aswellasSelvinandLipton(2003)found
Chapter1Generalintroduction
37
thattheriskofV.harveyiandV.alginolyticusinfectionisincreased.Recently,therehas
beenhighprevalenceofwhitefecesdisease(WFD;aprotozoaninfection)andAHPND
incidenceatthesameshrimpfarmingsites(Limsuwan,2010;Sriurairatanaetal.,2014).
Somboonetal.(2012)indicatedthatthehaemolymphandintestineofWFD-infected
shrimphashighnumbersofV.vulnificus.
Anthropogenic factors are shown to be associated with vibriosis in farmed shrimp.
Examplesoftheseanthropogenicfactorsincludethelackofpathogen-freebroodstock
screening,andtheuseofequipmentorfacilitieswithoutdisinfection(Chrisoliteetal.,
2008).
Theriskfactorsforvibriosisoutbreaksinfarmedshrimpdescibedabovewillbeusedin
designingourwork(Chapter3).
Chapter1Generalintroduction
38
Table 1.1 Reviews of risk factors for vibriosis in shrimp farming
Riskfactor Hostspecies Pathogenspecies
(1)Farminglocation
Differentgeographiclocation(betweenthewestandtheeastcoastalareaofIndia)
Penaeusspp. Vibriospp.(Gopal,2005)
Farminglocationnearhumancommunities
Penaeusspp. V.parahaemolyticus,V.vulnificus,V.alginolyticus(Mohneyetal.,1994),andV.cholerae(ReillyandTwiddy,1992)
Farminglocatedclosetoestuaries L.vannamei V.alginolyticus(WangandChen,2005)
Farminglocatedinorclosetoagriculturalareas
L.vannamei V.parahaemolyticus(Roqueetal.,2005)
(2)Climate
Cooltemperature(at20°C) P.stylirostris V.penaeicida(Goarantetal.,2000)
Warmtemperature(at32and34°C) L.vannamei V.alginolyticus(Chengetal.,2005)
Wetseason Penaeusspp. V.cholerae(ReillyandTwiddy,1992)
(3)Farmingmanagementpractices
FeedingshrimpwithuntreatedArtemia
Penaeusspp. V.parahaemolyticusandV.harveyi(Quiroz-Guzmánetal.,2013)
Highphytoplanktondynamics P.stylirostris Vibriospp.(Lemonnieretal.,2016)
Intensiveshrimpaquaculture Penaeusspp. V.cholerae(ReillyandTwiddy,1992)
Stressedshrimpandlargeamountsofthepathogeninponds
P.stylirostris V.nigripulchritudo(Mugnieretal.,2013)
Largeamountsofpondsediment Penaeusspp. V.nigripulchritudo(Goarantetal.,2006;Wallingetal.,2010),V.cholerae(Lekshmyetal.,2014),andVibriospp.(Thoetal.,2012)
Heavyorganicmatter Penaeusspp. V.choleraeandV.parahaemolyticus(Ganeshetal.,2010)
HighpH(>7)andhighsalinityandammonialevels
P.monodon
V.alginolyticus,V.parahaemolyticus,V.damsela,andV.anguillarum(HeenatigalaandFernando,2016)
Chapter1Generalintroduction
39
Table 1. 1 (cont.)
Riskfactor Hostspecies Pathogenspecies
Highersalinitylevel L.vannamei V.parahaemolyticus(LokkhumlueandPrakitchaiwattana,2013)
Highconcentrationofammoniainwater
L.vannamei V.alginolyticus(LiuandChen,2004)
Noapplicationsofeitherponddryinginsunlightorpondharrowing
Penaeusspp. Vibriospp.(Nimratetal.,2008)
DecreasesinthediversityoftheVibriocommunity
P.monodon Vibriospp.(Sungetal.,1999)
Usingwatersourcedfromthesea P.monodon V.harveyiandV.splendidus(Lavilla-Pitogoetal.,1990)
(4)Viralandprotozoandiseaseoutbreaks
OutbreaksofWSD P.monodon V.harveyi(Kannapiranetal.,2009),andV.alginolyticus(SelvinandLipton,2003)
OutbreaksofWFD L.vannamei V.vulnificus(Somboonetal.,2012)
(5)Anthropogenicactivities
Transmissionofpathogensviabroodstock,maturationandspawningfacilitiesintheshrimphatchery
P.monodon V.harveyi(Chrisoliteetal.,2008)
Chapter1Generalintroduction
40
1.5.3 Site-to-sitetransmissionofshrimpdiseases
Inshrimpfarming,thefollowingmodesappeartobetheimportantroutesinsite-to-site
diseasetransmission:long-distanceviamovementsofliveshrimp(eitherbroodstockor
shrimp seed), local spread via closeproximityof sites, and sharingofwater courses
(Figure1.7).However,notalltransmissioneventsfitthispatternandotherroutesof
transmissionexist.
Figure 1.7 Major routes in site-to-site transmission of shrimp diseases
Themost common route for transmitting diseases from site to site is long-distance
transmission.Withlong-distancetransmission,asusceptiblesitecanbeinfectedviathe
importationofinfectedshrimp.ThepandemicsofWSD,YHD,IHHNandTS(Diseasebox
2–4and6,respectively)infarmedshrimpintheUSAwereobviousexamplesofnational
epizooticsresultingfromthemovementsofliveinfectedshrimpseedandbroodstock
(Lightner,2003;Lightneretal.,1997).ItincludesthepotentialtransmissionofAHPND,
anewemergingoutbreakinshrimpfarming(OIE,2013a).
Note that this research (Chapters 4, 5 and 6) focuses on the domestic epizootics in
shrimp farming, where all site-to-site movements of live shrimp can serve as long-
distance transmission routes at the country scale. Although movements of frozen
shrimpproductscanleadtosomeinfections,e.g.WSSVandYHV(Nunanetal.,1998),
thesearenotincludedinthisresearch.
Hatcherysite
Nurserysite
Ongrowing site
SeaLong-distancetransmission
viashrimpseedandbroodstock movements
Unknowntransmission
Localtransmission
Localtransmission
Unknowntransmission
Chapter1Generalintroduction
41
Diseasebox6:Taurasyndrome
Taurasyndrome(TS)istheinfectionofshrimpbytaurasyndromevirus(OIE,2013b).Itwas
firstdetectedinfarmedshrimpinEcuadorin1992(Chaivisuthangkuraetal.,2016).TSwas
observedinTaiwanin1998duetointroducingTS-infectedshrimpfromepizooticcountries
(Tuetal.,1999).Diseasedshrimphaveapalereddishcolouration,witharedtailfanand
pleopods,andaresoft-shelled (Bonamietal.,1997;Lightneretal.,1995;Songetal.,2003).
Acutemortalityofshrimpispossiblewithinthreedays(ChunIandYenLing,2000).
Anotherpathwayforsite-to-sitediseasetransmission isvia localspread,whichoften
occursduetoanthropogenicactivities.InThailand,afewlarge-scalefarmsapplyclosed
recirculatingsystems inshrimprearing.Theremaining farms,however,mayconduct
waterdischargeorwaterexchange(Boydetal.,2017;Flahertyetal.,2000;Yaemkasem
et al., 2017). Although water exchange results in a decrease in the ammonia
concentration in shrimp ponds (Hopkins et al., 1993), where the exchange occurs
directlybetweenshrimppondsandnaturalwatercourses(e.g.canals,lakes,riversand
thesea),withoutproperwatertreatment, thiscancontributetowidespreaddisease
through hydrological connectivity (Anh et al., 2010; Pruder, 2004; Tendencia et al.,
2011). Additionally, the absence of installation of crab and bird fencing in shrimp
farmingcanaidlocalspreadofdiseaseduetophysicalproximityofsites,asdescribed
inBalakrishnanetal.(2011)andKumaran(2009).
Themovementof fomites (inanimateobjects such as farming facilities, vehicles and
staff’s clothes) aids local and long-distance transmission by introducing diseases to
susceptible sites (Rodgers etal., 2011).Corsin etal. (2005) found that therewasno
strongassociationbetweenpotentialfomites(sharingfarmingfacilitiesandstaff)and
disease spread in shrimp farming, despite fomites often being subject to disease
mitigationmeasures, such as the sanitation of incoming vehicles anddisinfection of
facilitiesinfarming(Bondad-Reantaso,2016;Dvorak,2009;Mohanetal.,2004;Yanong
andErlacher-Reid,2012).
Chapter1Generalintroduction
42
For modelling purposes, the final mode for disease transmission between sites is
unknown transmission with a usual exposure to risk factor but in which a major
transmissionpathwayisnotidentified.
Thenatureofshrimpdiseasesandtheirtransmission,aspresentedinthissection,are
importantinforminganepizootiologicalstudyforAHPNDandotherdiseases.Crucially,
a better understanding of the occurrence and transmission of infectious diseases in
farmedshrimpcanbeachievedwithmodellingapproaches.Amongthesemodels,this
studyisinterestedinnetworkmodelsandcompartmentalepidemicmodels,whichare
discussedinChapter2“Epidemiologicalandepizootiologicaltoolsfordesignofdisease
preventionandcontrolstrategies”.
1.6 Researchoutline
Thepurposeofthisresearchistodevelopepizootiologicaltoolstoevaluatethespread
ofAHPNDintheThaishrimpfarmingindustry.Itisimportanttoknowtheeconomicand
sociologicalimportanceofshrimpfarming,thecharacteristicsofThaishrimpfarming,
the occurrence of diseases and their transmission, and the disease prevention and
controlmeasuresthathavebeendeveloped.Thethesis,therefore,includesareviewof
the literature on the epidemiological and epizootiological tools for various sectors,
allowingthedevelopmentofeffectivetoolstopreventandcontrolthespreadofdisease
spreadindividualsiteandcountrylevels.
This first chapterhasbeenwritten to give a general introduction toepizootiological
toolsforAHPNDandotherknowndiseasesintheThaishrimpfarmingsector,andto
demonstratetheresearchoutlinehere.Theremainderoftheresearchaimsto:
- Investigate the risk factors for acute hepatopancreatic necrosis disease
(AHPND);
- Demonstratethestructureoftheliveshrimpmovementnetwork(LSMN),which
poses potential for site-to-site transmission of AHPND and other known
infectiousdiseases;
Chapter1Generalintroduction
43
- Identify connections in the LSMNposing ahigh risk for disease transmission,
leadingtowardsthedevelopmentofdiseasesurveillanceandcontrolalgorithms
forThaishrimpfarming;and,
- Model thedynamicsofAHPNDepizootics in shrimp farming sites,where the
modelconsidersbothlong-distanceandlocaltransmission.
Theresearchhasbeendivided intosevenchapters;anoverviewofthechaptersand
theirlinkageisgiveninFigure1.8.
Thefollowingchapter(Chapter2)discussestherelevantliterature.Itexplores:(1)study
designsinepidemiologyandepizoology,(2)graphornetworktheory,and(3)epidemic
modelsandnetworkmodels.
Inthethirdchapter,asarecentoutbreakinshrimpfarming,theriskfactorsforAHPND
wereinvestigatedbyanepizootiologicalsurveyatfarmlevel.Across-sectionalstudyis
described.ThisstudylinkedwiththedatafromtheSustainingEthicalAquacultureTrade
(SEAT),EUFP7researchproject. Importantly, thissurveydata, i.e.diseasemitigation
measures, farmingmanagementpracticesand thecumulativeAHPND incidence,has
alsobeenusedininterpretingresultsofChapter4andinmodellingthespreadofAHPND
ofChapter6.
Inthefourthchapter,graphornetworktheoryisappliedforthefirsttimetotheThai
shrimpfarmingsector.Thechapterdemonstratestheindustrysusceptibilitytoinfection
vialong-distancetransmission(i.e.liveshrimpmovement)basedontherealnetworkof
liveshrimpmovements inThailand (LSMN). It containsaquantitativeanalysisof the
LSMNincludingpropertiessuchasdegrees,averagepathlength,clusteringcoefficients
andassortativity.Theresultsofthischapterinformthefifthchapter.
Disease-controlalgorithmsaredevelopedtoidentifyhigh-riskconnectionsintheThai
shrimp farming network (Chapter 5). These algorithms include various network
centrality measurements (e.g. betweenness, eigenvector and degree), and their
capacities in reducing thepotential epizootic size in the LSMNaremeasuredby the
reachabilityofsites(innetworkterminology,nodes)andconnectedcomponents.The
Chapter1Generalintroduction
44
bestalgorithmcanbeusedasacontrolstrategy.Moreover,diseaseoutbreaksdonot
only leadtofinancial lossesforproducersandinterruptionofbusiness,buttheyalso
influenceannualgovernmentexpenditure(FAO,2013b).Thedisease-controlalgorithm
developedinthischapterwillthereforebeconcernedwithoperatingcostsaswellas
effectiveness.
Inthesixthchapter,thedynamicsofAHPNDepizooticsareexplainedusinganetwork-
basedepidemicmodel.TheresultsindicatetheseasonalityofAHPNDspread,andthe
effect of long-distance and local transmission on the AHPND epizootic dynamics in
Thailand;theyalsosuggestdiseasepreventionandcontrolmeasurestoexplore.
All the results of the research are discussed in the final chapter, including the
contributionof the research to theThai shrimp farming sector, andpotential future
work.
Chapter1Generalintroduction
45
Figure 1.8 Outline of the “Epizootiological tools for AHPND in Thai shrimp farming” research.
Networkmodelsandcompartmentalepidemicmodels
Diseaseepizootics
Shrimpfarming
Epidemiologicalapproaches
Epidemiologicalandepizootiological study
designs
Networkstructureanalysis
Controlstrategies
Chapter1Generalintroduction
Chapter2Literaturereviews
Chapter5Disease-controlalgorithms
Chapter3RiskfactorsforAHPND
Chapter4Structureofliveshrimpmovementnetwork
Chapter6Network-basedepizootic
modelforAHPND
Chapter7Generaldiscussion
Chapter1Generalintroduction
46
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Chapter 2 - Epidemiological and epizootiological tools for design of disease prevention and control strategies
N.Saleetid;D.M.Green;F.J.Murray
Preface
Thischapterdescribesthetheoreticalliteratureandempiricalevidencethatrelatesto
the development of disease prevention and control strategies. The chapter covers
epidemiological and epizootiological study designs: experimental, observational and
theoreticalstudies.Theseapproacheswillallowthe(1)investigationoftheriskfactors
foracutehepatopancreaticnecrosisdisease(AHPND)inChapter3(mostofthesehave
beenpreviouslybeenshowntobeusefulhistoricalinshrimpdiseases);(2)studyofthe
structureoftheliveshrimpmovementnetworkinChapter4;(3)designofthealgorithms
fortargeteddiseasesurveillanceandcontrol inChapter5;and(4)runningofnetwork-
basedmodelsofAHPNDepizooticdynamics inChapter6.The finalpartdescribes the
applicationtoAHPND.
Chapter2Epidemiologicalandepizootiologicaltools
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Chapter 2 - Epidemiological and epizootiological tools for design of disease prevention and control strategies
Advances intechnologyandinnovationhave ledtoan increase inshrimpfarming. In
turn, dramatic decreases in global shrimp production due to widespread infectious
diseaseshavebeenwidelyreported(FAO,2013;FAO,2016).Therehavethusbeenmany
attempts to design disease prevention and control measures, both for the early
detection of diseases, and for maintaining the sustainability of the shrimp farming
sector (Bondad-Reantaso et al., 2005). This chapter is intended to: (1) review the
epidemiologicalandepizootiologicaltoolsthathavebeenthemostusefulhistoricallyto
examine the distribution (pattern and frequency) and risk factors for disease
transmission among a population, and (2) identify gaps in current epizootiological
research into disease outbreaks in shrimp farming. Epidemiological studies cover
experimental,observationalandtheoreticalapproaches,however,thisthesisfocuses
on the two latter typesofepidemiological studiesbecause theyaremoreuseful for
determiningdiseasetransmissioninthewholeshrimpfarmingsites.
2.1 Experimentalstudies
Evaluatingriskfactorsfordiseaseisoneofthemajortasksinepidemiology(WHO,2016)
that can be conducted with experiments. Experimental studies are used where the
effect of exposure to a risk factor is evaluatedby assigning that exposure alongside
controlstoastudypopulationsuchasaclinicaltrialofnewdrugforwhitespotdisease
(WSD)inshrimp(Ocampoetal.,2014).Muchoftheexperimentalstudyofdiseaseshas
been done in the context of testing a particular hypothesis in the laboratory. For
example, theoccurrenceofWSD in farmed shrimpwas found tobeassociatedwith
chemicalcomponents,suchasanexposureof20mgL-1totalammonianitrogen(Liang
etal.,2016),alowwatertemperatureof27°C(Rahmanetal.,2006),andarapidchange
insalinityfrom22to14ppt(Liuetal.,2006).Theepizoologyofvibriosisinshrimpwas
studiedexperimentallytoassesstheirassociationwithpesticide-contaminatedshrimp
(Labrieetal.,2003),andthepresenceofWSDbeforevibriosis(Phuocetal.,2008).From
thisliterature,itisobservedthatsuchexperimentalstudieskeepotherenvironmental
Chapter2Epidemiologicalandepizootiologicaltools
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factorsconstant,while in fact thecomplexityof thenaturalenvironmenteffects the
occurrenceofdiseases.
2.2 Observationalstudies
Thesecondtypeofepidemiologicalstudiesisobservationalstudies(wheretheeffectof
exposuretoariskfactorisobservedbyexperimenterswithoutpriorassignmentofany
exposuretoastudypopulation)(Jepsenetal.,2004).Observationalstudiesoftentake
place in a natural environment to test multiple hypothesises, for example, where
multipleriskfactorsarethoughttobeassociatedwiththeoccurrenceofdisease.An
exampleofthiscanbeseeninCorsinetal.(2001).Usingacohortstudydesign,about
100 potential risk factors have been investigated for their association with WSD
occurringinfarmedshrimp(Corsinetal.,2001).Thisisoneofthethreemaintypesof
observationalstudiesasdescribedfollowing.
The threemain types of observational studies are cross-sectional, cohort and case–
controlstudies.Withdifferenttemporaldesigns,cross-sectionalstudiesaremainlyused
to determine prevalence, meaning the number of cases (diseased individuals) in a
population at a particular point in time. Cohort studies may be prospective or
retrospective.Whereas,case–controlstudiesaregenerallyretrospective(Mann,2003;
SongandChung, 2010).Among these, if apopulation sampled is alreadydefinedas
disease cases and controls, case–control studies show comparative advantages,
particularlythroughbeingabletolimittheeffectofconfoundingfactors(riskfactorsfor
adiseaseinnon-exposedindividuals,whichareassociatedwiththeexposureofinterest
inthestudiedpopulation)(Mann,2003;Salasetal.,1999).Todealwithanyconfounding
factor,casesandcontrolsarematchedusingspecificcriteriasuchastheageandgender
ofparticipants (McCreadieandScottishComorbidityStudyGroup,2002;Yusufetal.,
2004),orthegeographiclocationoffarmingsites(Boonyawiwatetal.,2016).
Investigations with case–control studies also minimise the incompleteness of data
collectionthat isoftencausedbydeathor inabilitytocontactcasesduringfollow-up
periods.Thisproblemhappensinprospectivecohortstudieswithalargesamplesize
(Holme et al., 2016; Swerdlow et al., 2015). In addition, since finding cases ismore
Chapter2Epidemiologicalandepizootiologicaltools
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expensiveincross-sectionalstudies,somebiasmayariseduetotherecruitmenteffort
needed to complete those studies, as demonstrated in Van Schayck et al. (2002).
Regardingtheselimitations,case–controlstudiesareaneffectiveandfeasiblestrategy
tofindriskfactorsfordiseases,comparedwithotherstudydesigns(Diomidus,2002;
Schlesselman,1982;SchulzandGrimes,2002).
Toillustratethisfurther,thefollowingthreeexamplesdrawuponthethreemaintypes
ofobservationalstudies:cross-sectional,cohortandcase–controlstudies.Thefirsttwo
examplesstudiedWSD;anotherstudiedAHPND.Lookingatotherdiseasesandtherisk
factorsforthem,however,willprovideideasandhypothesesforthiswork,aswellas
forotheremergingordifferentdiseaseslikeAHPND.
Across-sectionalstudywasconductedbyTendenciaetal.(2011)toinvestigatetherisk
factorsforWSDinfectioninshrimpfarmsinthePhilippinesduringa14-monthsurvey
of the disease. Cases (WSD-infected farms) were identified by the
principalclinicalsignofWSD,i.e.whitespotsappearedonthebodyoffarmedshrimp,
or by a polymerase chain reaction (PCR) test for confirmation of WSD. The result
stronglysuggestedthatfeedingfreshmolluscstoshrimpwasamajorriskfactorofWSD
infection.Itwasexpectedthat,asfilterfeeders,molluscscouldactasWSSVcarriersby
obtainingthepathogensinsoilandwaterwithshrimppredationofthemolluscsleading
to the transferof thesepathogens. This researchhasaweakly temporal association
betweenthepresenceofdiseaseandtheriskfactorbecausebothdataweremeasured
simultaneously. Thus, itmaydifficult to determinewhether thepresenceof disease
followedexposure to the risk factor in timeor exposure to thepotential risk factor
resulted from the presence of disease (Song and Chung, 2010). However, there are
manyotherpublishedworksthatsupportthefindingthattheuseoffreshmolluscsand
fishinshrimprearingisapotentialriskfactorforWSDandotherviraldiseases(Hameed
etal.,2002;Vijayanetal.,2005)
As a forward-looking approach, a prospective cohort study investigates individual
groups moving forwards from exposure to a risk factor to the later presence (or
absence)ofastudydisease(GrimesandSchulz,2002).Inshrimpfarming,aprospective
cohortstudywasconductedbyCorsinetal. (2001)to investigatetherisk factors for
Chapter2Epidemiologicalandepizootiologicaltools
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WSD.Theauthorsdesignedthecohortstudywiththe24shrimppondsofaVietnamese
farm, and a six-month follow-up of the study disease. Cases (infected ponds) were
definedbyaPCRtest.Themainresultindicatedthatearthenshrimppondsestablished
closetotheseawereassociatedwithanincreasedriskofWSDpresence.Thisresearch
couldutilise a cohort studybecauseof the shortproduction cycleof farmed shrimp
(aroundsixmonths)andtheuseofasmallnumberofepizootiologicalunits(24shrimp
pondsofafarm).
In contrast to prospective cohort studies and cross-sectional studies, a case–control
study is a retrospective or backward-looking approach (Hoffmann and Lim, 2007;
Pearce,2012).Study individualsarealreadydivided into twogroups:cases (denoted
diseased individuals) and controls (referred non-diseased individuals), and then
exposuretocandidateriskfactorsinthepastineachgroupisexamined(Mann,2003).
Recently, a case–control study was designed to identify the risk factors for AHPND
(Boonyawiwatetal.,2016).Caseswereobtainedfromreportingofdiseaseoccurrences
by farmers to local staff of the Thailand Department of Fisheries. During the study
period, the pathogenic agent of this disease remained unknown (the PCR test was
unavailable), but the cases were confirmed by histopathology based on the major
AHPNDsignsgiveninNACA(2014).Thisresearchsuggestedfivefactorswhichrelated
toincreasedriskofAHPNDoccurrenceatshrimppondlevel,i.e.theuseofchlorinefor
water treatment, theavailabilityofawater reservoir, thecultureofpredator fish in
waterpreparationponds,thestockingofmultipleshrimpspecies,andincreaseddensity
ofshrimpstocking.Acase–controlstudydesignwasmostefficientforthisresearchfor
tworeasons.First,beinganewdiseaseatthetime,AHPNDcasesremainedrareorhard
toconfirm.Second,thepresenceofAHPNDamongshrimpfarmingsiteswassuspected
to be related to multiple risk factors, especially in respect to farm management
practices.
This section has described the main types of observational study designs for
epidemiologythatareusedtoevaluatetheriskfactorsforshrimpdiseases.Togainmore
knowledgeofdisease spreadand todesigneffectivediseasepreventionand control
measures,many recent epidemiological studies have alsoused theoretical approach
Chapter2Epidemiologicalandepizootiologicaltools
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withmathematicalmodelstoenhancetheunderstandingofdiseaseepidemicdynamics.
Thesewillbediscussednext.
2.3 Theoreticalstudies
In theoretical study design of epidemiology, the dynamics of disease spread can be
determinedbymeansofawidevarietyofmathematicalmodelssuchascompartmental
epidemicmodels,mass-actionmodels,networkmodelsandindividual-basedmodels.
Thissection,therefore,givesdetailsofthemathematicalmodellingapproaches.These
providemoreunderstandingofthechallenges instudyingthedynamicsof infectious
diseases transmitted among shrimp farming sites in Thailand, since these epizootic
dynamics remain poorly understood. Some of the discussed epidemic modelling
approacheshaveneverbeenappliedtoshrimpepizoology.
2.3.1 Compartmentalepidemicmodelsformicroparasitesversusmacroparasites
Mostdiseaseoutbreaksinshrimpfarmingarecausedbymicroparasites(Flegeletal.,
2008). Severe infections frommacroparasites occur less frequently due to chemical
treatmentandgoodmanagementpractices in farming.Nevertheless,macroparasites
canserveasavectorinthetransmissionofmicroparasites,leadingtolargeepidemics
(Loetal.,1996;Vijayanetal.,2005;Zhangetal.,2008).Tomodeltheirspreads,the
natureofthelifecycleandhowitisappropriatelymodelledareimportant(Anderson
andMay,1991).
Thelifecyclesofmicroparasitescontrastwiththoseofmacroparasites.Amicroparasite
cancomplete its lifecycle(multiply)withinan individualhost,whileamacroparasite
utilisesoneormorehosts,orispartlyfreeliving.Amicroparasite(viruses,bacteria,fungi
and protozoans) is a very small organism but amacroparasite (e.g. arthropods and
worms) is largerandcanbecounted.Nevertheless, itshouldbenotedthatalthough
somemicroparasitesare“micro”inscale,theirnaturetendstobe“macro”inmodelling.
Anexampleisfree-livingciliateprotozoans.Recently,theyhavebeenshowntocause
ectoparasitic diseases of farmed shrimp in Iran, i.e.mostly by species of the genus
Zoothamnium (Afsharnasab, 2015), and of farmed Nile tilapia in Saudi Arabia, i.e.
Trichodinamaritinkae,T.centrostrigeataandT.frenata(Abdel-Bakietal.,2017).
Chapter2Epidemiologicalandepizootiologicaltools
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To model the dynamics of microparasite transmission, the Kermack-McKendrick
mathematicalmodeliswidelyused(Diekmannetal.,1995;KermackandMcKendrick,
1927). Table 2.1 shows three well-known mathematical model types and their
applicationtomicroparasiteinfections(ofteninhumandiseases),inwhichanindividual
can be in one of three compartments:𝑆 (susceptible), 𝐼 (infectious) or𝑅 (recovery,
removed,deathorquarantine).Thus,theyarealsocalledcompartmentalmodels.
Table 2.1 Three well-known compartmental models and their application to microparasite infections
Model Host Microparasitedisease
𝑆𝐼𝑅 Human Measles(Bjørnstadetal.,2002;SattenspielandDietz,1995;Shulginetal., 1998), influenza (Brauer, 2008), dengue fever (Feng and Velasco-Hernández, 1997), chickenpox,measles,mumps and influenza (Allen,2008;Coburnetal.,2009;FengandVelasco-Hernández,1997),andZikavirus(Bewicketal.,2016)
Livestock Foot-and-mouth disease (Hagenaars et al., 2011; Heath et al., 2008;Keeling,2005b)
Salmon Furunculosis(Ogutetal.,2005)
Shrimp Whitespotdisease(Hernandez-Llamasetal.,2013;LotzandSoto,2002),andtaurasyndrome(Lotzetal.,2003)
𝑆𝐼𝑆 Human Sexually transmitted diseases, i.e. hepatitis B (Kribs-Zaleta andMartcheva,2002)andGonorrhoea(Grayetal.,2011)
𝑆𝐼 Human SexuallytransmitteddiseasesHIV/AIDS(BozkurtandPeker,2014;Lloyd-Smithetal.,2004)
𝑆𝐼𝑅modelsareappropriatefordiseaseswithfullimmunityorpermanentremovalby
death while 𝑆𝐼𝑆 models (susceptible-infectious-susceptible) have been used for
diseaseswithlittleornoimmunity(Allen,1994;BlyussandKyrychko,2005;Fengand
Velasco-Hernández, 1997; Keeling and Eames, 2005). Hence, in the 𝑆𝐼𝑆 models an
individualcanbecomesusceptibleafterthatindividualrecoversfromaninitialinfection.
Finally, 𝑆𝐼 models apply to diseases that remain infectious for life and are never
“removed”(Kimetal.,2013;Tassier,2013).Becausethesecompartmentalmodelsapply
toindividuals,theinterpretationofthematanindividualscalediffersbetweensmaller
andlargerscales.
Chapter2Epidemiologicalandepizootiologicaltools
68
A unique compartmental model may not be sufficient to model all the various
mechanisms for disease transmission. In many cases of studying epidemiological
patterns (e.g. spreadbymultiple transmission routes), researchershaveaddedextra
compartments intothestandardmathematicalmodeltogainmoreunderstandingof
therealisticdiseasetransmission(Johnsonetal.,2016;Ngetal.,2003;TienandEarn,
2010). Two examples using 𝑆𝐼𝑅𝑆 models where an individual achieves temporary
immunity,and𝑆𝐸𝐼𝑅models(withcompartmentterm“𝐸”denoted“exposed”)where
duringanincubationperiodanindividualhasthediseasebutisnotyetinfectious,such
asforinfluenza(DegliAttietal.,2008),tuberculosis(Maraisetal.,2004)andmeasles
(Robinsonetal.,2014).Theseadditionalcompartmentsextendtheentirelifecyclesof
pathogensintothemodellingofdiseaseoutbreaks.
Inanothertypeofmodellingshrimpdiseases,macroparasiteinfectionsprimarilycausea
decreaseingrowthrateandstrength,andanincreaseintheoccurrenceofdeformityinfish
(WilliamsandBunkley-Williams,2000).Macroparasitesthatarecommonlyfoundinfarmed
fish and shrimp include helminths, e.g. monogeneans, cestodes and nematodes
(Domínguez-Machínetal.,2011;Soler-Jiménezetal.,2016),andcopepods,e.g.sea lice
(Igboelietal.,2014).Duetothecomplexlifecyclesoftheseorganisms,MayandAnderson
(1979)proposethatcompartmentalmodelsareunlikely tobehelpful forstudyingtheir
transmission. They state that thedynamicsofmacroparasite infectionsaremuchmore
dependentonthenumberofparasitesinahost.Thus,modellingbasedonthenumberof
hosts, parasites, and free-living infective stages becomesmore efficient than standard
compartmentalmodels(MayandAnderson,1979).
For the outbreak of AHPND (a microparasite infection) in Thai shrimp farming, the
𝑆𝐸𝐼𝑅𝑆compartmentalmodelisausefultooltomodeltheepizooticdynamicsatsite
level.Fourcompartmentsinthe𝑆𝐸𝐼𝑅𝑆model:susceptible(𝑆),exposed(𝐸),infected(𝐼)
and removed (𝑅) cover the actual patterns of AHPNDoccurrence in shrimp farming
sites.Othermodelsmayworkwell,however.Forexample,macroparasitemodelscan
workatsitelevelifthewithin-siteprevalencevariesalot.Alternatively,simplermodels,
suchas𝑆𝐼𝑅𝑆models,canbeused.Additionally,compartmentalmodelsofthespread
of infectious disease that utilise mass-action or network approaches might be
applicable.Thesearediscussedinthefollowingsub-section.
Chapter2Epidemiologicalandepizootiologicaltools
69
2.3.2 Mass-actionversusnetworkmodels
Mass-action and network models have important roles in modelling and assessing
diseasetransmissionbothbetweenpersonsoranimalfarmingsites,andwithinapond.
Thedistinguishingcontrastbetweenmass-actionandnetworkmodelsisoutlinedhere.
2.3.2.1 Mass-actionmodels
Mass-action models assume disease transmission in a homogenous population of
humans, animals or farming sites (Hethcote and Van Ark, 1987; Nold, 1980). For
example,thefollowing𝑆𝐼epidemicmodeldivides𝑆and𝐼intotwocompartmentsthat
are differentially susceptible and infectious. The rate of individual in class 𝑖 being
infectedattime𝑡is:
where𝑆> + 𝐼> = 𝑁> atalltimes,theparameters𝛼and𝛽areaconstantcontactrateand
transmissionrateperunittime,respectively.
In addition, the direction of disease transmission is neglected in most mass-action
models. This does not include themass-actionmodel that is naturally directed. The
directionof transmissioneffectsepidemicdynamicsbecausean individualcanactas
eitherasourceorasinkofinfection,orasbothasourceandsinkofinfection(Chaves
andHernandez,2004;Wesolowskietal.,2012).Forexample,intheThaishrimpfarming,
thepotentialpathwayoftransmissionthroughliveshrimpmovementsisfromhatchery
sites(sourcesof infection)tonurserysitesortoongrowingsites(sinksfor infection),
althoughtherearealsoasmallnumberofupstreammovements(fromongrowingsites
to hatchery sites) to reproduce new generations of shrimp, such as for genetic
improvementprogrammes(seeFigure2.1:thisstructurehasbeenappliedtoChapters
4,5and6).Thus,neglectoftheserealepidemiologicalpatternsmayleadtoover-or
under-estimationofdiseaseepidemicsinmass-actionmodels(Meyers,2007).
dSi
dt= �↵iSi
X
j
�jIj
Chapter2Epidemiologicalandepizootiologicaltools
70
Figure 2.1 Potential pathway for disease transmission via live shrimp movements in Thailand. The main pathway is downstream towards the ongrowing sites. There are a small number of upstream movements (from ongrowing sites to seed-producing sites) for reproducing new generations of shrimp.
2.3.2.2 Networkmodels
Owingtomodel-basedapproaches,severalrecentepidemicmodelsondiseaseshave
takenthereal-lifediseasespread,i.e.theheterogeneityofinfectioninapopulation,and
thedirectionoftransmissionintoconsideration.Thesearetypicallyknownasnetwork
models (Funk et al., 2010; Perisic and Bauch, 2009). The major difference between
networkmodelsandmass-actionmodelsisthatthenetworkmodelscancapturethe
reality that the population tends to be heterogeneous with non-random mixing
(HethcoteandVanArk,1987;Nold,1980).Keeling(2005a)statedthatinmass-action
modelseachinfectedindividualcantransmittoallotherindividuals,whilethedisease
spreadinnetworkmodelsrequiresconnectionsbetweenindividualsthatrepresenta
transmissionrouteofdiseasestobespecified.Hence,thechanceoftransmissionfrom
infected individuals in network models is dependent on contact networks that are
generallydegree-heterogeneous(Keeling,2005a;Volzetal.,2011).
Insupportofthisview,Woolhouseetal.(1997)explainedtheheterogeneityofinfection
withthe80/20rule.Therulesuggeststhatoften20%ofindividualscontributeatleast
80%oftheinfectioninthewholepopulation.Therefore,whereasthenetworkmodels
are estimated using the 80/20 rule, the mass-model assumption disregards the
heterogeneityofinfectioninapopulation,i.e.inthenumberofconnectionsbetween
Hatcherysites(1st seed-producingsites)
Nurserysites(2nd seed-producingsites)
Ongrowing sites
Chapter2Epidemiologicalandepizootiologicaltools
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individuals(Finkenstadtetal.,2002;Germannetal.,2006;Kaneetal.,1999),andthe
timeofhost-parasiteinteractions(Milleretal.,2012).
Basically,themodellingofanetworkisbasedongraphtheory,inwhichanetworkrefers
to a group of sites (network terminology, nodes) connected by direct or indirect
connections. Networks with direct connections potentially allow the modelling of
diseasespreadtoaddressthesourcesandsinksfordiseasetransmissionbecausethe
directionofconnectionsisalreadyknown(GatesandWoolhouse,2015).Inmanycases,
networkmodelsarereconstructedthroughsimulatednetworks,whilerealnetworksare
lessoftenmodelledduetotheunavailabilityofreliablenetworkdata,orlackofadata
recordingsystem(KeelingandEames,2005).Asimplesimulatednetworkisthatofa
nodeconnectingwithtwoneighboursinaring.Additionally,WittenandPoulter(2007)
presented network types that have often been used in epidemicmodelling: random,
lattice, Watt–Strogatz small world and Barabasí–Albert scale-free networks.
DemonstrationsofthesesimulatednetworksareshowninFigure2.2.
Figure 2.2 Epidemic network models often are often characterised by these five simulated networks. All networks contain 20 nodes. The probability of each connection is 0.4 for drawing the random network, and 0.1 for drawing the small-world network.
Thetheoreticaldistinguishingpropertiesinsimulatednetworksareusefultodetermine
disease spread inmany real networks including farmed animalmovement networks
Ringnetwork Randomnetwork
Latticenetwork Small-worldnetwork
Scale-freenetwork
Ringnetwork Randomnetwork
Latticenetwork Small-worldnetwork
Scale-freenetwork
Ringnetwork Randomnetwork
Latticenetwork Small-worldnetwork
Scale-freenetwork
Ringnetwork Randomnetwork
Latticenetwork Small-worldnetwork
Scale-freenetwork
Chapter2Epidemiologicalandepizootiologicaltools
72
(Christley et al., 2005a; Kiss et al., 2006). Poisson degree distribution, a major
characteristicofrandomnetworks,islessoftenfoundinreality(Newmanetal.,2002),
butmanyrealnetworksarefoundtohaveotherproperties,suchasthoseofsmall-world
networks and scale-free networks. A small-world network is characterised by high
clustering,andshortpathlengths(distancesbetweensites)(WattsandStrogatz,1998).
As measured by a clustering coefficient, clustering is defined as the tendency for
trianglesofconnections toexist innetworks (Newman,2008).Small-worldnetworks
withshortmeanpathlengthsalsolinktothesmall-worldexperimentofMilgrametal.
(1992)whostudiedthe‘sixdegreesofseparation’theory.Theyproposethatindividuals
can get a piece of information (or a disease) via a connection of nomore than six
intermediates (Milgram et al., 1992; Watts, 1999). Infection within small-world
networksiscommonlyfast,mainlyduetotheirshortpathlengths(thenumberofpaths
traversed between a site pair) (Kiss et al., 2006; Newman, 2008). For scale-free
networks, degree distributions lie on a power-law form𝑃(𝑘)~𝑘/0 (Barabasi, 2009;
Pastor-SatorrasandVespignani,2001).Mostsitesinascale-freenetworkhaveasmall
number of connections, but a few sites have a large numbers of connections. The
heterogeneity in these site degrees becomes more interesting in terms of disease
preventionandcontrol,particularlyindesigningacontrolstrategyatthemosthighly
connectedsites(Barthélemyetal.,2005).
In termsof theapplicationof theoretical studies inepidemiology,mostof themcan
representdiseasespreadinwholepopulationsandleadtothedevelopmentofefficient
diseasesurveillanceandcontrolmeasures. Inordertoachievetheaimofthisthesis,
therefore, network models for targeted disease surveillance and control will be
addressedinthenextsub-section.
2.3.2.3 Networkmodelsfortargeteddiseasesurveillanceandcontrol
Targeted disease surveillance and control using a risk-based approach is one of the
potentialstrategies tomakethe farmingsectormoresustainable (PeelerandTaylor,
2011).Forfarmedshrimp,accordingtoNACA(2017),theAsianaquacultureindustryhas
establishedtwoimportanttypesofsurveillancetoprotectthehealthoffarmedaquatic
animals:activeandpassivesurveillance.Activesurveillanceisundertakenwiththegoal
Chapter2Epidemiologicalandepizootiologicaltools
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of investigating targeted pathogens, while passive surveillance utilises laboratory
samplessubmittedfordiseasetestingpurposes(BurgessandMorley,2015).Thereare
several limitations with these surveillance programmes, however, such as a lack of
suitable resources for surveillance, and the effective diagnosis of diseases (Bondad-
Reantasoetal.,2005).Cost-effectiveinterventionsfordiseasecontrolscanbeimproved
iftargetedsurveillanceandcontrolisdevelopedandimplemented.
Forthespreadofdiseaseonsocialoranimalmovementnetworks,theideaoftargeted
surveillance and control is that high-risk connections serve strongly as a potential
transmission route for infectiousdiseases; and thus their removal from thenetwork
leads to a decrease in transmission (Duan et al., 2005; Green et al., 2012; Lou and
Ruggeri,2010).Bajardietal.(2012)arguethattargetingdiseasesurveillanceandcontrol
approaches based on centralitymeasuresmay fail tominimise the epidemic in the
network due to temporal fluctuations. Measures of centrality, however, play an
importantroleintargetingdiseasesurveillanceandcontrolforfarmedanimaldiseases,
asshowninTable2.2.
Table2.2alsodemonstratesthatanoptimalcentralitymeasureforonenetworkmay
not performwell for others, because such networks have particular structures. For
example, thealgorithmbasedonbetweenness centrality relatedwell to the specific
properties of the network of livestock movements in France, which displayed a
scale-free network and a large connected component (a giant strongly connected
component or GSCC) over the network (Rautureau et al., 2011). Accordingly, these
centralitymeasures,whichalreadyapplytofarmedanimalnetworks,areusedtoform
potentialcandidatedisease-controlalgorithmsintheLSMN(Chapter5).
Chapter2Epidemiologicalandepizootiologicaltools
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Table 2.2 Centrality measures studied in five networks of farmed animal movements resulting in optimal strategies for targeted disease surveillance and control
Centralitymeasure LivefishinScotlanda
LivestockintheUKb
LivestockinItalyc
LivestockinArgentinad
LivestockinFrancee
Degree x x x* x* x
Betweenness x x* x x x*
Community-bridging x
Greedy x*
Eigenvector x x
Closeness x
xExaminedforthisnetwork*TheoptimalcentralitymeasureaLivefishmovementsinScotland(Greenetal.,2012).bLivestockmovementsintheUK(Ortiz-Pelaezetal.,2006).cLivestockmovementsinItaly(Nataleetal.,2009).dLivestockmovementsinArgentina(Aznaretal.,2011).eLivestockmovementsinFrance(Rautureauetal.,2011).Degree=thenumberofconnectionsofeachnodeBetweenness=thenumberofshortestpathsthatgothroughnodes,oralternatively,connectionsCommunity-bridging=theconnectionsthatlinkbetweentwosub-networksGreedy=theabilityofconnectionsintermsofthegreatestreductiontoeithermeanandmaximumreachEigenvector=thedegreecentralitiesofallneighbouringnodesconnectedtoaspecifiednodeCloseness=thenumberofshortestpathsfromaspecifiednodetoallneighbouringnodes
WefinishthissectionbyremarkingthatinThailandnoworkhasbeendonetostudy
shrimpepizoologywithnetworkmodelling.Tofillthisgap,thenetworkstructureoflive
shrimpmovementsofThailandisanalysed,andthepotentialofthenetworkinrespect
todiseasespreadevaluatedinChapter4.Inaddition,disease-controlalgorithmsforthe
liveshrimpmovementnetworkofThailandareexaminedinChapter5intermsoftheir
efficacyinreducingthepotentialepizooticsize.
While themodellingof thedynamicsofdiseaseepidemicsbecomesmoreandmore
complex, another epidemiological tool, individual-based modelling, might be an
appropriatetool.Thistypeofmodelisdiscussedinthenextsub-section.
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2.3.3 Individual-basedsimulationmodels
Individual-basedmodellingisarobusttoolforepizoology.Thekeydistinguishingfeature
of individual-based modelling is the ability to track individuals in a population. In
individual-based models, the transmission of diseases can be tracked at microscale
interactions (e.g.disease spreadbetween infected sitesandsusceptible sites,where
“individual”referstothesite)inordertointerprettheepidemicdynamicsofawhole
populationinaspecifictimingandlocation(Al-Mamunetal.,2016;Kisjesetal.,2014).
Individual-basedmodelsareincreasinglyimportantforexaminingmanyseveredisease
outbreaks,andsupportingdecision-makers in thedevelopmentofcontrol strategies.
Theyhavebeenmainly applied in recent epidemiological studiesof humandiseases
suchastuberculosis(CardonaandPrats,2016),andanimaldiseasessuchasvibriosis
(Paillardetal.,2014)
An important reason for choosing individual-basedmodels is that the modelling of
infectious disease transmission has become very complex (Gu et al., 2003). The
complexitiesinmodellingepidemicdynamicsareillustratedinthefollowingexamples.
Demographics (the age or gender of persons) affect the spread of influenza type A
(Arimaetal.,2013;Donaldsonetal.,2009;Quandelacyetal.,2014).Pathogenssuchas
influenza,malariaordenguecontainmultiplestrainscausingdiverseepidemiological
patternsinpopulations(LourençoandRecker,2013).Individualsmaycarryoutvarious
behaviourssuchasavoidingcontactwithinfectedpersons(VanSegbroecketal.,2010),
and choosing longer distances of animalmovements for aquaculture (Keeling et al.,
2001).
Inaddition,manybiologicalprocesseshaveastochasticnature.Forexample,asiteis
exposedtoadisease,butisnotinfected.BlackandMcKane(2012)indicatethatthere
hasbeena recent increase in thenumberof individual-basedmodelsandstochastic
models for predicting infectious disease dynamics. To incorporate stochasticity in
individual-basedmodels,Rattanaetal.(2013)usedtheGillespiealgorithmtodetermine
nextevents according toevent timesdistributedexponentially. Insteadofusing this
exponentialdistribution,Verguetal.(2010)usedagammadistribution(i.e.apositively
Chapter2Epidemiologicalandepizootiologicaltools
76
skeweddistribution)toevaluatethechangesfrominfectioustorecoveredstates.This
servedexplicitlytosimulatethebiologyofindividuals.
An important concept that is established inmany individual-basedmodels is thatof
metapopulationconcept.Thisconceptemphasisesthat individualsoftencharacterise
twoormore structuredpopulations (Levin,1974).Keeling etal. (2010) indicate that
individual-basedmodelsthatdonotmaintainthis individuals’structureoverestimate
thespatialspreadandpotentialsizeoftheepidemic.Anexampleofindividual-based
metapopulationmodelsisdiseasestransmissioninfishfarmingsitesmodelledontwo
structuredpopulations:thesiteandthefish(Green,2010).Thisstructurealsoappears
infarmedshrimpproduction,inwhichsitestuctureissimplified,givingrisetoasingle
levelmodel,asintheChapter6ofthisthesis.
Insummary,thedynamicsofdiseaseepidemicsareaffectedbytheheterogeneityof
individualsandthestochasticnatureofthetransmissionprocess.Thesedynamicsare
difficult to determine by using equation-basedmodels, but it can be achievedwith
individual-basedmodels.
2.4 Applicationtoacutehepatopancreaticnecrosisdisease(AHPND)
ThischapterpresentstoolsthatarepotentiallyusefulforexaminingAHPNDspreadin
Thailand.AHPND,anewdiseasewithhighmortalitiesoffarmedshrimp,hasoccurred
ineasternThailandsince2011/2012fromwhenceitwastransmittedtoothershrimp
farmingareas.Theseepizooticsresultedinthelowestproductionoffarmedshrimpin
thetenyearsfrom2003to2013(ThailandDoF,2016).
InordertoinvestigatetheriskfactorsforAHPNDthatstillhadanunknownetiologyat
the time of commencement of this study, farmers' knowledge of shrimpdiseases is
directlyrelevantinordertodevelopacasedefinitionforAHPND.Thaifarmershavelong
experienceinfarmingshrimp,combinedwiththeexperienceoflossesduetodiseases
(Tookwinas et al., 2005).When infections occur, farmers are able to submit farmed
shrimp fordiagnosisofdiseases inmanyaquaticanimalhealthservicesbothprivate
and governmental laboratories; the laboratory results give the farmers better
Chapter2Epidemiologicalandepizootiologicaltools
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knowledge about shrimp diseases. Thai farmers are therefore able to differentiate
AHPNDcasesfromothershrimpdiseases.
For Thai shrimp farming, data on live shrimp movements from source sites to
destinationsitesareavailable(SEAFDEC/MFRDMD,2016).Thiskindofdataisimportant
for studying recent epizoology. The potential transmission of AHPND, and other
infectious diseases of farmed shrimp, can therefore bemodelled as a network that
containsall sitesandtheirconnections.Theanalysisof thenetworkstructureallows
diseaseepizooticstobeevaluated,andassistsinthedevelopmentofcontrolstrategies
atacountrylevel,suchastargeteddiseasesurveillanceandcontrol.
IntherealpatternofAHPNDspread,latentperiodsareevidentinsite-to-siteAHPND
transmission. During these periods, farmed shrimp are infected by AHPND, but no
clinicalsignsofthatdiseaseappearatsitelevel(Tranetal.,2013).Nonetheless,these
exposed sites remain infectious. After a site is infected with the disease, different
fallowing periods are presented given different farming management practices for
removingthedisease.Then,thesitesstartanewcropandbecomeatriskofre-infection
(thisevidencewasobservedintheepizootiologicalsurveyreportedinChapter3).This
indicates that the 𝑆𝐼 model for AHPND epidemic dynamics requires extra
compartments,i.e.exposed(𝐸)andremoved(𝑅)states.
Another epidemiological tool that can be applied for studying AHPND epizootic
dynamics in Thai shrimp farming is the compartmental, individual-based epidemic
model.Thismodelishelpfulintestingcontrolstrategiesbychangingparameterssuch
as lower rates of long-distance transmission, denoting better disease control
arrangementsinthecountry(Rileyetal.,2003).
To conclude, the epidemiological and epizootiological tools that we present in this
chapter are useful for shrimp disease epizoology in Thailand. Not only are these
epizootiologicaltoolssuitableforAHPND,buttheyarealsousefulforotherinfectious
diseasesoffarmedshrimpsuchasWSD,YHDandothervibriosis.
Chapter2Epidemiologicalandepizootiologicaltools
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Chapter 3 - Evaluating risk factors for transmission of acute hepatopancreatic necrosis disease (AHPND) in the Thai shrimp farming sector
N.Saleetid;D.M.Green;F.J.Murray
Preface
Buildingontheauthor’smaster’sdegreestudyin2012/2013,theinvestigationofrisk
factorsforsite-to-sitetransmissionofAHPNDisintensivelyassessedinthischapter.An
extra phase of data collection (Phase 4: Cross-checking of the risk factors) was
implementedandmoreadvancedstatisticalapproacheswereapplied,insofarasthese
aretechnicallyadmissible.Intheextraphase,thedatawerecollectedusingface-to-face
interviewswithninekey informants.Moreadvancedstatisticalanalyses(i.e.receiver
operating characteristic curveandcross-validationanalysis)wereapplied than those
usedintheoriginalmaster’sthesis.Thesurveydata,i.e.farmingmanagementpractices
and the cumulative AHPND incidence, has also been used in interpreting results of
Chapter4andinmodellingthespreadofAHPNDofChapter6.
Atthetimeofdatacollectionforthisobservationalepizootiologicalstudy,thecauseof
theinitialoutbreakofAHPNDwasunknown.Thus,thedesignationofcasesandcontrols
wasbasedontheAHPNDdecisiontree,whichwasdevelopedasanepizootiologicaltool
inthecross-sectionalstudy.Notethatthischapterisdesignedforpublication.Thisstudy
utilisedthedatafromtheSustainingEthicalAquacultureTrade(SEAT),EUFP7research
project.
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Chapter 3 - Evaluating risk factors for transmission of acute hepatopancreatic necrosis disease (AHPND) in the Thai shrimp farming sector
3.1 Abstract
Inthisstudy,theriskfactorsforsite-to-sitetransmissionofAHPNDwereevaluatedin
Thailandusinga cross-sectional approach.Anunbiased sample frameof206 shrimp
farms (previouslyparticipated in theSustainingEthicalAquacultureTrade (SEAT),EU
FP7 researchproject)wereengaged in four consecutive structured surveys in2013–
2014. The outcome led to the development of a decision tree for AHPND case
determinationandriskestimationusingunivariateandunconditionallogisticregression
analysis.
Interviewsweresuccessfullyperformedwith143oftheabove206shrimpfarms(70%).
35%ofthe143mettheAHPNDcasedefinition,withahigherproportionintheeast.
Southern farms showed a delay in AHPND onset, and large-scale farms that usually
invested inmore biosecurity resources than others also showed a delayed onset of
AHPND. The cumulative incidence of AHPND in southern and large-scale farms
increased sharply after the first occurrence AHPND, however. Two risk factors for
AHPNDtransmissionwerefound:earthenpondswerelessriskywithanoddsratio(𝑂𝑅)
of0.25(95%CI0.06–0.8;P-value=0.02)comparedwithshrimprearinginfullyplastic-
lined ponds; and the absence of pond harrowingwas higher riskywith an𝑂𝑅 of 3.9
(95 % CI1.3–12.6; P-value=0.01) compared with the presence of pond harrowing.
ThesefindingsimplythattheThaishrimpfarmingindustryshouldenhancebiosecurity
systems,andthatasimplegoodfarmingmanagementpractice,suchasharrowingpond
bottomwhichareacommonpracticeofshrimpfarminginearthernponds,mayprotect
farmsagainstAHPND.
3.2 Introduction
Acutehepatopancreaticnecrosisdisease(AHPND)isthemosteconomicallydamaging
epizooticpandemictoaffectthefarmedpenaeidshrimpsectorsincetheoutbreakof
whitespotsyndromeinthe1990’s(Flegel,1997).In2013thetotalannuallosseswere
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estimatedasUSD5billion(Rosenberry,2013).AHPND,alsodescribedasearlymortality
syndrome (EMS)prior to the recentdetectionofabacterialpathologicalagent (OIE,
2013;Thitamadeeetal.,2016)wasfirstdetectedinChinain2009.Itwassubsequently
reported in Vietnam in 2010,Malaysia and Thailand in 2011 (Eduardo andMohan,
2012),Mexicoin2013(Nunanetal.,2014)andthePhilippinesin2014(Leobertetal.,
2015),whereasmassmortalities attributed toVibrio species in Indian shrimp farms
werenon-AHPND(Kumaretal.,2014).
AHPND(mostThaifarmerscalledthisdisease"EMS"or"EMS/AHPND")wasreportedin
Thailand at first time in the east central provinces in late 2011 (FAO, 2013) and in
southernproducerprovincesinlate2012.Ineachinstance,theincidencerosesharply
in the months following the first detection, resulting in widespread precautionary
fallowingofpondsastheprimaryfarmerresponse(Flegel,2012).Althoughthedisease
affects a wide range of important commercial penaeid shrimp species, including
Litopenaeus vannamei, Penaeus monodon and P. chinensis farmed over a range of
productionintensities(OIE,2013),thehighestriskoflossappearstobeassociatedwith
moreintensivefarmingpractices(FAO,2013),thenormforL.vannameiproduction.
Theformer“EMS”titledescribestheoccurrenceofmassmortalities(>70%)duringthe
first 35 days of culture in newly prepared ponds (FAO, 2013b; Flegel and Lo, 2014;
Lightner et al., 2012; Thitamadee et al., 2016). AHPND, meanwhile, describes the
characteristicdegenerativepathologyinthevitaldigestiveandglandularmidgut-organ.
Toxin-producingstrainsofVibrioparahaemolyticushavebeenconfirmedasaprimary
causativeagentTranetal.(2013).Thesepathogensalsohadahighchanceofantibiotic
resistance(Hanetal.,2015a;Hanetal.,2015b;Laietal.,2015).SeveralVibriospecies
are zoonotic (Austin, 2010). Fortunately, the three strains of V. parahaemolyticus
implicatedinAHPNDinfectioninThailanddonotappeartopresentapublichealth-risk
(Chonsinetal.,2016;Kondoetal.,2014).
TheclinicalsignsofAHPNDdonotdiffersignificantlyinsomecasesfromthoseofshrimp
infectedwithdifferenttypesofvibriosis.Forexample,asahepatopancreaticinfection,
V.harveyicausesnecrosisofthehepatopancreaticcells,andathickerabnormalbasal
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laminathaninnormalshrimp(Jiravanichpaisaletal.,1994;Tranetal.,2013).Clearcase
definitionisthereforeveryimportanttoevaluatetheriskfactorsforAHPNDoccurrence.
Arangeofstudieshaspositivelycorrelatedloss-severitywithvariousenvironmentalrisk
factors.ThegeneralabundanceofVibriospp.andotherpathogensinshrimphasbeen
linkedtohighphytoplanktonlevels(Petersonetal.,2010)andpHlevel(>8.2,Costaet
al.,2010),andAkazawaandEguchi(2013)foundsuchacorrelationbetweenelevated
pHandAHPNDinMalaysia.InChina,outbreaksofAHPNDhavebeenassociatedwith
elevatedsalinitylevelsandolderponds(Panakorn,2012).Co-locationofsemi-intensive
andintensivefarmingsystemsinsouthernVietnampointedtoanincreasedprobability
ofAHPNDmortalitiesinintensivesystems(FAO,2013).Therehasbeenagenerallackof
systematicepizootiologicalstudiesunderpinningsuchobservations,however.
Takingacross-sectionalapproach,theThaishrimpfarmingsitesweresampledovera
studyperiodtoseekpairedgroups,withorwithoutAHPNDclinicalsigns.Bothgroups
were identified and comparedon thebasis of potential causal attributes. The study
aimedtoassess(1)theassociationofenvironmentalandfarm-managementriskfactors
withthegeographicalprevalenceandincidenceofputativeAHPNDcasesinThailandin
order to (2) draw inferences regarding its transmission and to suggest possible
mitigationstrategies.
3.3 Methods
3.3.1 DevelopmentofacasedefinitionanddecisiontreeforAHPND
SincethecausalagentofAHPNDwasnotknownatthistime,casedefinitions inthis
studywerebasedonclinicalsignsdescribed intheAHPNDdiseaseadvisoryofNACA
(2014),throughkeyinformantandfarmerperceptions(Section3.3.4),andareviewof
other secondary literature on Thai and global AHPND outbreaks. Based on this
information,an‘AHPNDcasedecision-tree’wasconstructedaroundfourspecificand
measurableindicators:(1)dateofonsetofthefirstclinicalsignsofAHPND,(2)theage
of the affected shrimp, (3) mortality rates, and (4) three characteristic and easily
observedclinicalsignsofAHPNDinfection:i.e.locationofdeadshrimp,diseasedshrimp
behaviourandthepresenceofapale/whitishhepatopancreas.Toassistdifferentiation
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in this final step, picture cards showing gross AHPND whitish and atrophied
hepatopancreas pathology (FAO, 2013; NACA, 2014) were shown alongside those
showing clinical signs of other high-prevalence shrimp diseases (white spot disease,
yellow head disease, taura syndrome, vibriosis and infectious hypodermal
haematopoietic necrosis virus:Murray et al., 2013). The picture cards are shown in
AppendixAattheendofthisthesis.
3.3.2 CandidateriskfactorsforAHPNDoccurrenceatfarmlevel
Candidate farm-level risk (i.e. independent) factors for AHPND occurrence were
reviewedfromsecondarydata(AkazawaandEguchi,2013;FAO,2013;Panakorn,2012)
and key informant opinion (Thai Department of Fisheries or DoF staff and farmers:
Section 3.3.4). In addition, potential risk factors were also mined from an earlier
‘IntegratedFarmerSurvey’(Section3.3.3).Adiverserangeof24factors(Table3.1)was
short-listed covering farm scale and location, farming experience, management
practices,farminfrastructureandwatermanagementcharacteristics.
Table 3.1 Candidate risk factors for AHPND occurrence at farm level
Mainfactor Candidateriskfactors
Farmscaleandlocation Farmscaleandregion
Farmingexperience Ageoffarm
Managementpractices Ageofpondaffected,ponddryingduration,stockingdensity,pondharrowingbeforestocking,effluenttreatment,sedimentremovalandsedimentfate
Farminfrastructure Ongrowingpondtype,maximumwaterdepth,cleanwaterstorage,alternativecleanwaterstorageandgrowingpond,andsedimentpond
Watermanagementcharacteristics
Watersource,dischargemethod,waterstoragemethod,recirculateandreusewater,maximumwaterreplacement,waterexchangefrequency,maximumsalinity,averagepHandaveragealkalinity
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3.3.3 Sampledesign
An ‘Integrated Farmer Survey’ (IFS: Murray et al., 2013), separately enumerated
betweenDecember2010andJuly2011(i.e.immediatelypriortotheonsetofAHPND),
providedasampleframeof206farmsforthisresearch.Theprimaryselectionphaseof
206samplefarmswasbasedondistrict-wiseproductiondatacollectedfromprovincial
DoF offices between 2009 and 2014 (Thailand DoF, 2016), followed by randomised
selectionoffarmclustersandindividualfarmswithinclusters.Theresultsofthecurrent
study are therefore expected to be generalisable to wider Thai shrimp production
conditions.
Inmore detail, this framewas based on amulti-phase, random sampling approach
stratified on farm-scale and location, i.e. being conducted across the principal Thai
farming areas in east central (Chachoengsao and Chanthaburi provinces) and south
Thailand (mainly Suratthani province and some areas in Songkhla province). Farm
selection was stratified on three scale levels—large, medium and small—based on
indicatorsofbusinessownership,labourpattern,farmmanagementandthenumberof
ongrowingponds,asshowninTable3.2(Murrayetal.,2013).
Table 3.2 Criteria used for classifying Thai shrimp farms into three scales: small, medium and large (from Murray et al., 2013)
CriteriaFarmscale
Small Medium Large
Ownershipofbusiness Householdorextendedfamily
Householdorexternalowner
Corporate(i.e.jointstockcompany)
Full-timelabour(non-family)
<3Full-timelabour Yes Yes
Management Householdorextendedfamily
Householdorexternalsalariedmanager
Salariedmanager
Numberofongrowingponds
<3 >2 >2
Chapter3Cross-sectionalstudy
96
AsakeyvariabletodetermineAHPNDspread,thelocationoffarmscorrespondedto
differentonset-timesforAHPND,i.e.firstintheeastfollowedbythesouth,andalsoco-
variedwithscaletosomedegree,asfarmstendedtobelargeronaverageinthesouth.
Informalorganisationoffarmersthroughshrimpfarmerclubsalsotendedtobemore
advancedinthesouth(Kassametal.,2011)and,therefore,hypothetically,alsotheir
abilitytocoordinatecollectiveactioninresponsetodiseaseoutbreaks.
3.3.4 Surveydesign
Primarydatacollectionwasbasedonfouriterativesurveyphasesconductedbetween
March2013andDecember2014,asdescribedbelow.All interviewswereconducted
with permission of the farmers in the sample-frame. The interviews took place at a
convenienttimeforthefarmers,andwereconductedintheThailanguagebythemain
authorwhoisaThainativespeaker.
Phase1Brieftelephonesurvey.Anattemptwasmadetocontactbytelephoneall
206 farmers in the sample-frame. With a short 15–20 minute structured survey,
respondentswereaskedaboutdiseaseproblemsandpossibleassociatedriskfactorson
theirfarmssincethefirstreportsofAHPNDinThailandatthelateof2011.Questions
coveredthetiming(month)ofthefirstAHPND-typelosses,clinicalsigns,numbersof
pondsaffected,mortality rates, ageofdiseased shrimp,managementpractices, and
measurestakentomitigatediseasetransmissionorrecurrence,asshowninAppendixB
attheendofthisthesis.
Phase2In-depthface-to-faceinterview. This more in-depth structured survey
incorporatedthepre-prepareddecision-treeanddiseasecardsdescribedabove.Atotal
of143farmerswhohasrespondedatPhase-1farmersprovidedthesample-framefor
thisphase.Arandom-selectionmethodwasusedtoselect14–15farmsforinterviewin
eachofthethreefarm-scaleandthreeprovinces(i.e.twoeasternandonesouthern).
An interview was arranged in their farm. It took approximately 30–45minutes per
interview.Notethatthenumberoffarmsineachscalecategorywaslessbalanceddue
tolimitsontotalnumbersineacharea.
Chapter3Cross-sectionalstudy
97
Phase3Follow-upgap-fillinganddatavalidation. Thisphasewasconductedwith
Phase-2farmersbyphonebetweenJuneandSeptember2013.Itincludedcollectionof
additionaldataonstockingdensitycharacteristics,ageofaffectedponds,ponddrainage
andsludgeremovalpractices,andwaterqualityconditions(pH,salinityandalkalinity)
Phase4Cross-checkingoftheriskfactors.Thefinalphasewastoexplorefarmers’
perspectivesontheriskfactorsinrespecttoAHPNDobtainedfromthecross-sectional
analysis,andanychangeinlocalshrimpfarmingpractices.Severalopen-questionswere
usedtoasknineskilledpersonsfromwithinthesampleframeinDecember2014.An
interviewwasarrangedintheirfarmandtookapproximately50minutesto1hour.This
interviewallowedthefarmers’opinionsandbeliefsinallrespects.
3.4 DataAnalysis
3.4.1 DescriptiveanalysisoftheoccurrenceofAHPNDandotherdiseases
TheprevalenceofputativeAHPNDcases,andotherdiseases,wasplottedagainstthe
locationand farm-scale stratificationvariables. The cumulativemonthly incidenceof
newAHPNDcaseswasalsoplottedagainstthesamevariables,calculatedasthenumber
ofnewcasespermonthtimedividedbythenumberofthetotalrespondingpopulation
atrisk ineachstratificationcategory.Basedonthetimingofthefirstreportedcases
within the sample frame, we used two location-specific exposure times for case
definitions.First,casesintheeasternprovincesrangedfromJanuary2012toMay2013.
Second,casesinthesouthernprovincesrangedfromDecember2012toMay2013.
3.4.2 StatisticalanalysisoftheriskfactorsforAHPND
TheaimofthestatisticalanalysiswastoevaluatetheriskfactorsforAHPNDoccurrence
at farm level. All statistical analyses were conducted within the 𝑅 Programme
Environment (R foundation for statistical computing, 2015). The risk of AHPNDwas
estimated as an odds ratio (𝑂𝑅); this measure is often used to investigate risks
associatedwithrarediseases(SchmidtandKohlmann,2008):
Chapter3Cross-sectionalstudy
98
𝑅GHI7JGK = CaseswithriskfactorNon-caseswithit
𝑅;78[\76 = CaseswithoutriskfactorNon-caseswithoutit
𝑂𝑅 = 𝑅GHI7JGK𝑅;78[\76
where𝑂𝑅 >1:riskfactorforincreasedriskofAHPND
𝑂𝑅 <1:riskfactorfordecreasedriskofAHPND
𝑂𝑅 =1:riskfactorisindependentofAHPNDpresence.
Usingtwo-stepmethods,the24candidatevariables(Section3.3.2)werescreenedusing
univariatetestsfollowedbymultivariatetests.Fortheunivariatetests,thesignificance
ofthe𝑂𝑅wasassessedthrough95%confidenceintervals(CI),Fisher’sExactTestor
binary logistic regression. Multivariate tests were done using unconditional logistic
regressionmodelstoevaluatethepotentialriskfactorsthatwereobtainedfromthe
univariatetestsabove.AllvariableswithaP-value<0.1inunivariatemodelswerekept
totestusingbackwardstepwise(variableswereremovedfromthemodel)regression
procedure.Givenseveral competingmodels, theAIC (Akaike’s InformationCriterion)
wasusedasacomparisontool,inwhichthebetter-fittingmodelshowedalowervalue
ofAIC(Akaike,1974).
Thepredictiveperformanceofthemodelswasevaluatedintwoways:
(1) Cross-validation technique:Using a random-selectionmethod, farm samples in a
groupofcasesandcontrolsweredividedintotwoapproximatelyequal-sizesubsets:D1
andD2.ModelswereconstructedonD1asatrainingset,andthepredictiveabilityofthe
modelswastestedusingD2asatestingset(Morietal.,1999).Then,wecomputedthe
proportion of observations, which were correctly predicted. Cross validation was
performedbothwaysround(swappingthesets),andanaverageoftheaccuracieswas
calculatedandreported.
Chapter3Cross-sectionalstudy
99
(2)Receiveroperatingcharacteristic(ROC)curve:TheROCapproachwasusedtojudge
theperformanceofmodelsintermsofpredictingAHPNDoccurrencewithtwopossible
outcomes:cases(diseasedfarms)andcontrols(non-diseasefarms).Weusedarangeof
different cut-off points obtained from estimated probabilities (log odds) to define
predictedcasesandnoncases.TheycoordinatesontheROCcurveforamodelwere
derived as true positives (sensitivity), and the x coordinates were derived as false
positives(1-specificity)ateachcut-offpoint.BasedontheareaundertheROCcurve
(AUC), themodel was classified to be either an informativemodel (AUC>0.5; the
predictiveresultofmodelisbetterthanarandommodel)oranuninformativemodel
(AUC=0.5;thepredictiveresultofmodelisnotdifferentfromarandommodel)(Alonzo
andPepe,2002;KumarandIndrayan,2011).
Toaidunderstanding,theoverallmethodologyisshowninFigure3.1.
Chapter3Cross-sectionalstudy
100
Figure 3.1 A flow chart of the methodology used in evaluating risk factors for transmission of acute hepatopancreatic necrosis disease (AHPND) in Thai shrimp farming. The survey design contained four major phases. Data analysis evaluated the risk factors for AHPND in three steps.
3.5 Results
3.5.1 IdentificationofAHPNDcasesandcontrols
3.5.1.1Case-identificationAHPNDdecisiontree
Acase-identificationAHPNDdecisiontreewasdevelopedforthisstudybecause,atthe
time,AHPNDwasadiseaseofunknownetiology.Four indicatorsofputativeAHPND
infectionwereincorporatedintothisdecisiontree,resultinginfarmsbeingassignedto
one of three mutually exclusive infection categories: higher or lower probability of
Surveydesignandenumeration Dataanalysis
Phase1 Brieftelephonesurvey
Phase2 In-depthface-to-faceinterviews
Phase3 Telephonesurveytofollow-upgap-fillinganddatavalidation
Step1:Caseandcontrolclassificationusingacase-identificationAHPNDdecisiontreeStep2:Descriptiveanalysis(diseasestatusreport,cumulativeincidencetrends)Step3:Statisticalanalysis
- Calculationofoddsratio(!")andconfidenceinterval(CI)
- Unconditionallogisticregressionanalysis- Modelvalidations(crossvalidationandROC
approach)
Phase4 Cross-checkingoftheriskfactors
Chapter3Cross-sectionalstudy
101
AHPNDinfection,ornoAHPND(Figure3.2).Justificationsfortheindicators,constituting
fourtiersinthedecision-treewereasfollows:
Tier1DateofonsetofthefirstAHPNDclinicalsigns.Theonsettimingofputative
caseshad tooccurafter the first reportedcasesofAHPNDofficially reportedby the
ThailandDepartmentofFisheries(DoF)indifferentregions, i.e.thefourthquarterof
2011(OctobertoDecember)ineasternprovinces(FAO,2013),andthefourthquarter
of2012insouthernprovinces,accordingtooursurvey.
Tier2Ageofaffectedshrimp.BasedonthereportsofFlegelandLo(2014)andFAO
(2013),any infectionthatoccurredbeyond35daysafter firststockingofpost-larvae
wasexcludedfrombeingaputativeAHPNDcase.
Tier3Mortalityratesofinfectedfarm.AkazawaandEguchi(2013)reportedaverage
mortalityratesof70–80%in‘AHPNDaffectedpondsinalargecommercialshrimpfarm
inMalaysia(AkazawaandEguchi,2013)whilstmortalitiesapproaching100%havebeen
observedinothercountries(EduardoandMohan,2012).Consequently,caseswith70–
100%mortalityratewereclassedashavinghigherprobabilityofAPHNDinfectionwhilst
farmswithratesbetween10–70%wereclassifiedashavinglowerAHPNDprobability.
Tier4MultiplevisibleclinicalsignsoftheAHPNDpathology.BasedontheNACA
advisory (2014),casesclassifiedas lowerorhigherprobabilityAHPNDcases inTier3
were subjected to further confirmation according to the following additional
behaviouralgrosspathologicalsigns:
(1) Mortality location: Mortalities concentrated around the pond edge and pond
bottom.
(2)Shrimpbehaviour(spiralorswirlingswimmingtoedgeofpondandturningbelly-up),
and
(3)Pale/whitish/atrophyofthehepatopancreas(HP;alsoHPhardtocrushbyhand),
anddiscolourationofabdominalmuscle(opaque;pinkishorwhite).
Chapter3Cross-sectionalstudy
102
Havingdefinedhigherand lowerprobabilitycases fromTier3, thehigherprobability
casesneededtohaveonly(3)describedabovetobeconfirmed,whilsttwo,oneofthese
was(3),ormoreclinicalsignswererequiredtoconfirmlowerprobabilitycases.Inboth
instances,failuretorecogniseanyoftheseclinicalsignsresultedindemotionofthecase
toanon-AHPNDlosscause.Thecasesmayhaveco-infectionofothershrimpdiseases
suchaswhitespotdiseaseandyellowheaddisease.
Figure 3.2 The AHPND decision tree for determination of higher AHPND probability, lower AHPND probability, and no AHPND. The decision was based upon four tiers including the significant behavioural gross pathological clinical signs of AHPND.
3.5.1.2Completedinterviews
Interviewswere completed for 143 of the 206 farms in the original sample (70%).
Reasonsfornon-responsewereasfollows:48farms(76%)didnotrespondtocallsor
Timingoffirstsymptoms East- BeginningatOctober2011orSouth- BeginningatOctober2012
Ageofaffectedshrimp≤35dayspoststocking
Mortalityrate70–100%
Mortalityrate<70%
Mortalityrate<10%
Ifthepathological
condition(3)met
If≥ 2pathological
conditions met,oneoftheseistheclinicalsign
(3)
HigherAHPNDprobability
LowerAHPNDprobability
NoAHPND
Tier1
Tier2
Tier3
Tier4
Outcomes
NoNo
Pathological clinical signs(1)Mortalitylocation:Mortalitiesconcentratedaround thepondedgeandpondbottom.(2)Shrimpbehaviour (spiralorswirlingswimmingtoedgeofpondandturningbelly-up)(3)Pale/whitish/atrophyofthehepatopancreas (HP;alsoHPhardtocrushbyhand),anddiscolourationofabdominalmuscle(opaque;pinkishorwhite)
Chapter3Cross-sectionalstudy
103
theirnumberswerenolongervalid;15farms(24%)hadceasedtoengageinshrimp
farming(Table3.3).
Table 3.3 The outcome from the telephone survey (Phase 1) followed by face-to-face interviews (Phase 2)
Region East South
TotalProvince Chanthaburi Chachoengsao Suratthani Songkhla
farmscale S M L S M L S M L S M L
Completedinterview
22 7 3 43 9 0 27 25 4 0 0 3 143
Non-responseforcalling
4 2 0 7 2 0 6 10 0 0 0 4 35
Invalidcontactnumber
3 0 1 3 1 0 4 1 0 0 0 0 13
Notengagedinshrimpfarming
3 2 0 3 1 0 5 0 0 0 0 1 15
Total 32 11 4 56 13 0 42 36 4 0 0 8 206
S=smallfarm;M=mediumfarm;L=largefarm
3.5.1.3Caseandcontrolsamples
Basedonthedecision-treeoutcomes,the143respondingfarmswereeachassignedto
oneofthreegroups:(1)51wereclassedashavingahighprobabilityofAHPND,(2)55
farmsreportednodiseaselossorclinicalsignsconsistentwithotherdiseases,whilst(3)
theremaining37farmshadanindeterminatestatus,i.e.low-probabilityofAHPND.To
increasestatisticalpower,weomittedthese37farmswiththeindeterminatestatusof
group3fromfurtheranalysis.Thefirsttwogroupswereassignedascaseandcontrol
groups,respectively(Table3.4).
Chapter3Cross-sectionalstudy
104
Table 3.4 Cross-tabulation of outcomes for case and control samples
FarmScale
GeographicLocationTotal
Eastregion Southregion
Case Control Case Control Case Control
Small 26 24 5 14 31 38
Medium 8 3 9 12 17 15
Large 2 0 1 2 3 2
Total 36 27 15 28 51 55
3.5.2 DescriptiveepizoologyofAHPND
3.5.2.1Reportofdiseasestatus
ThediseasestatusofoursamplefarmsisshowninFigure3.3.AHPNDpresentedamajor
diseaseproblemforshrimpfarmsinthesampleframeinbothregionsofThailand,with
ahigherAHPNDoccurrenceof43%(36of84)ofsamplefarmsintheeastcomparedto
25%(15of59)ofsamplefarmsinthesouth.Consistentwiththeearlieronsetofthe
disease in the east, Chanthaburi province had the highest AHPND prevalence,
accountingfor44%(14of32)ofsamplefarmsinChanthaburi,followedby42%(22of
52)ofsamplefarmsinChachoengsao,and37%(15of56)ofsamplefarmsinSuratthani.
The figure also shows that farms infected with white spot disease also had a high
probabilityofAHPNDinfection,asfoundinsamplefarmsinChanthaburiandSuratthani.
FarmswithoutAHPNDclinicalsignswereinfectedwithwhitespotdisease,yellowhead
disease,taurasyndromeandwhitefecessyndrome.Theresults,therefore,providedan
overview of the disease problems facing the Thai shrimp farming sector during this
epizootiologicalsurvey.
Chapter3Cross-sectionalstudy
105
Figure 3.3 Report of disease status stratified according to geographic location and farm-scale between January 2012 and May 2013. AHPND was the main disease problem for Thai shrimp farming. Other diseases included white spot disease, yellow head disease, taura syndrome and white feces syndrome.
3.5.2.2Cumulativeincidencetrendsgraphed
Intheeasterndistricts,thecumulativeAHPNDincidencewas43casesper100farmsin
17monthsortwocasesper100farm-months,andinthesouthwas25casesper100
farmsinsixmonthsorfourcasesper100farm-months(Figure3.4).Itwasseenclearly
thatboththesouthernlocationandthelarge-scalevariablesshowedadelayofAHPND
onset compared with remaining factors (the eastern location, and the small- and
medium-scalevariables),buttheircumulativeincidenceincreasedsharplyafterthefirst
incidenceofAHPND.
0%
20%
40%
60%
80%
100%
Small Medium Large Small Medium Large Small Medium Large Small Medium Large
Chanthaburi Chachoengsao Suratthani Songkhla
East South
Numbe
roffarms(%)
Farmlocationandscale
22%
39%
6%
33%
17%
83%
100% 16% 40% 5% 29% 50% 100%
25%
59% 60%
69%
26%
29%
29%
13%
50%
Chapter3Cross-sectionalstudy
106
(a) (b)
Figure 3.4 The cumulative incidence of AHPND between January 2012 and May 2013, accounting to two regions (a) and three farm-scales (b).
3.5.3 RiskfactorsforAHPNDtransmissionatfarmlevel
The results of the univariate analysis are summarised in Table 3.5. Two of the 24
variableswereassociatedwithAHPNDatfarm-levelatthe0.05significancelevel.The
firstvariablewastheearthenpond–asignificantriskfactorforAHPNDwithan𝑂𝑅of0.25
(CI 0.06–0.8; P-value=0.02); the second was the absence of pond harrowing–a
significantriskfactorforAHPNDwithan𝑂𝑅of3.9(CI1.3–12.6;P-value=0.01).These
twovariablesweremodelled in furtheranalysis,whereas theremaining22variables
whichwerenotpredictiveofAHPNDaccordingtotheseunivariatetests(P-value>0.1)
wereexcluded.
JAN DEC2012
JAN MAY2013
051015202530354045 East
South
Rateper100
JAN DEC2012
JAN MAY2013
Small-scaleMedium-scaleLarge-scale
051015202530354045
Chapter3Cross-sectionalstudy
107
Table 3.5 The statistically significant risk factors for AHPND with odds ratios (𝑶𝑹s) and 95 % confidence intervals
Variable CaseNo. ControlNo. P-value 𝑶𝑹(CI)
Ongrowingpondtype(1)
Earthenpond(2) 32 460.02 0.25(0.06–0.8)
Linedpond 14 5
Pondmanagement(1)
Nopondharrowing(2) 33 240.01 3.9(1.3–12.6)
Pondharrowing 7 20
Note–(1)Fisher’sexacttestand(2)Referencelevel
FurtheranalysistoevaluateriskfactorsforAHPNDwasperformedusingunconditional
logistic regression models. Variables with a P-value<0.1 were kept to be run in
backwardstepwiseunconditionallogisticregressionmodels.Themodelsonlyincluded
casesinthedatawherealldatafieldsofthetwovariables(ongrowingpondtypeand
pondmanagement)werecompleted.
Theresultsoftheunconditionallogisticregressionmodelsthatfittedthedatabestare
showninTable3.6.ThefirstunconditionallogisticregressionmodelhasthelowerAIC
value.Thefirstnestedmodelcontainedongrowingpondtypeandpondmanagement
(AIC=102.19);thesecondmodelcontainedpondmanagement(AIC=107.29).
Chapter3Cross-sectionalstudy
108
Table 3.6 Unconditional logistic regression analysis of risk factors for AHPND
Model Variable CaseNo. ControlNo. Exp(coefficient)(1) SE(coef)
Model1
AIC=102.19
Ongrowingpondtype
Pondmanagement
39 41
0.19
4.35
0.67
0.47
Model2
AIC=107.29
Pondmanagement
3.95 0.52
Note–(1)Maximumlikelihoodestimationoddsratios
Formodelvalidation,thecross-validationanalysisispresentedinTable3.7.Themean
percentage correct of the two models obtained from the unconditional logistic
regressionisaround65%accordingtoourdatasets(referredtoas“D1”and“D2”).
Table 3.7 Cross-validation results on the AHPND models obtained from unconditional logistic regression
Model TestingonD1 TestingonD2 Mean
Model1:Ongrowingpondtypeandpondmanagement
68% 63% 66%
Model2:Pondmanagement 65% 63% 64%
UsingtheROCapproach,bothmodelswereplottedfortheirabilitytopredictAHPND
presenceat farm level.Theproportions inabinaryoutcome(diseaseornon-disease
farm)weremodelledbyusingapredictionof the logoddsandusingvariouscut-off
pointstodefinethepredictedoutcomes.Thetruepositives(sensitivity)ofthemodel
werepresentedontheverticalaxis,andthefalsepositives(1-specificity)wereshown
onthehorizontalaxis.
TheROCresultsarepresentedinFigure3.5.TheareaundertheROCcurve(AUC)ofboth
modelswascirca68%,i.e.ahighervaluethananuninformativemodel(AUCof0.5).
Chapter3Cross-sectionalstudy
109
Figure 3.5 ROC curves for AHPND models. Model 1 with two variables: ongrowing pond type and pond management. Model 2 with one variable: pond management. Both models obtain the AUC > 0.5 (better results than random). The diagonal line indicates the prediction from a random model.
TheseriskmodelsofAHPNDandtheirvalidationimpliedthat,inthecompletesubset,
thereweresignificantinteractionsbetweenpondharrowingpractice,ongrowingpond
typeandtheincidenceofputativeAHPNDcasesoccurringatfarm-level.Withthesetwo
riskfactors, itcanbesuggestedthatshrimpfarmersshouldapplypondharrowingas
oneofthemostimportantfarmingpractices.Thissuggestionfollowsthecomputation
ofthe𝑂𝑅whichinterpretsthatthesamplefarmswheretheabsenceofpondharrowing
beforestockingwereinfectedwithAHPND3.9timesmoreoftenthanthesamplefarms
thatappliedpondharrowing.
3.6 Discussion
The initial AHPND distribution in Thailand during January 2012 and May 2013 was
examinedinthesampleframeofthisobservationalepizoologystudy.Thefirstincidence
occurredintheeasternprovincesinJanuary2012,anddelayedincidenceinthesouth
in December 2012, at a higher cumulative incidence. A need for improvements in
biosecurity in Thai shrimp farming is implied through this research, given that the
incidenceofAHPNDoccurredinallfarmscales,eventhelargecommercialfarmswhich
generallyinvestmorebiosecurityresourcesinshrimpfarmingthanothers.
In this research, the identified risk factors for AHPND transmission emphasise the
importance of environmental farming managements. One of these risk factors is
1- Specificity 1- Specificity
Sensitivity
Model1:AUC=0.71 Model2:AUC=0.64
Chapter3Cross-sectionalstudy
110
earthenponds to raiseshrimp.Earthenpondswitha largeareaofpondsoilplayan
important role in shrimp farming production. For example, they provide a higher
capacity toaccumulateandabsorbnutrients (nitrogenandphosphorus) andorganic
matter (i.e.uneaten feed, faecesandmoribundshrimp),comparedwith linedponds
(Burfordetal.,2003;Funge-Smith,1996).Moriarty(1997)proposesthatearthenponds
providealargerhabitatformicroorganisms,whichisamajormechanismtoenhance
thefoodwebwithinponds,whilelinedpondshavealimitedcapacityinthisregard.The
accumulatedsediment,however,mayexceedthecapacityofpondstodecomposethe
nutrients,causingpoorwaterquality,andtoxicitytofarmedshrimp,i.e.fromnitriteand
ammonia (Avnimelech and Ritvo, 2003; Boyd et al., 2002; Hargreaves, 1998).
Predominantly, shrimp farm sediment is one of the important habitats for vibrios
(Lekshmyetal.,2014;Thoetal.,2012;Wallingetal.,2010),includingthepathogenic
agentofAHPND(Kongkumnerd,2014).
Incontrast,linedpondsincreasetheriskofAHPNDinfection.Thitamadeeetal.(2016)
notethattheutilisationofpondliningisaneffectivetoolfordiseaseprevention,but
fully lined ponds may contain gaps in the plastic sheets from installing aerators or
feedingtrays,andduetotheshortlifetimeofplasticsofaround2–5years.Theseleaks
allowanaerobicorganismstogrowunderneaththeplasticsheetsandincrementtherisk
of AHPND. The disadvantage of pond lining has been stated by Boyd (2014), i.e.
phytoplankton blooms, low-alkalinity water, and high amounts of sludge (organic
matter).Hence,linedpondsarealsolikelytoberelatedtoenvironmentalproblemsin
shrimpfarming.
Withearthenponds,thefarmerscanfullymanagetheanaerobicconditionsofAHPND
pathogens, and other comparative organisms through proper pond management
techniques,suchasliming,ponddrying,andpondbottomharrowing.Accordingtoour
research,notperformingharrowingbeforestockingwasafactorthatincreasedtherisk
of AHPND transmission. This finding is explained by the advantages of harrowing in
enhancing the shrimppondenvironment.Whenpond soil is exposed to theair, soil
respirationincreases(BoydandPippopinyo,1994;EgnaandBoyd,1997;Xinglongand
Boyd,2006).Predominantly, itcontributestoa loweramountoftoxicgases,suchas
hydrogensulphideandnitrite, inpondsoilbecausethesetoxicgasesareoxidisedto
Chapter3Cross-sectionalstudy
111
non-toxicforms(Adhikarietal.,2012;Boydetal.,2002).Therefore,bothahabitatof
facultative anaerobic bacteria such asV. parahaemolyticus (Youngren-Grimes et al.,
1988) and toxic gases should be decreased in shrimp ponds that are harrowed. In
addition,pondharrowingwithponddryingandprobiotics loadingisabletoenhance
decompositioninsediment,andtoeliminatethenumbersofVibriolivinginsediment
efficiently(Boyd,2003;Moriarty,1998;Moriarty,1999;Nimratetal.,2008).DeSchryver
etal.(2014)alsosuggestthatgoodmicrobialmanagementwithinshrimppondscanbe
anexcellentstrategyforAHPNDpreventionratherthanusingdisinfecting.
Theuseofacase-identificationAHPNDdecisiontreetodeterminecaseandcontrolwas
discussedhere.Thecase-identificationAHPNDdecisiontreeisaflexibleandintelligent
epizootiological tool: it supports epizoology in terms of being a quicker and less
expensiveanalysis. Furthermore, the case-identificationAHPNDdecision tree canbe
adaptedwhenthereisrecurrenceofAHPND,orthegrosssignsofdiseasepathologycan
bechangedwhenthere is incidenceofothernewdiseases. Identifyingcaseswithan
AHPNDdecisiontreemaybelessaccuratethanbyusinglaboratoryhistopathologyand
PCRtestingasdiagnostictools.Themainreasonisthatthiscase-identificationAHPND
decisiontreewasdevelopedforadiseaseofunknownetiology,thuswehavetohave
anidentifiedpathogeninordertohaveadefinitivecasedefinition.
Thisresearchhasseverallimitations.Thepossibilityforself-selectionbiasmayarisein
the survey design because the sample farms may have lower operating costs,
lower quality facilities, or misunderstanding of appropriate biosecurity practices,
meaning that the sample farms are more likely to get infections because they are
inherently riskier. We found, however, that self-selection according to biosecurity
practiceshadonlyasmallbiasonriskestimations.Asseenbythecumulativeincidence
ofAHPND(Figure3.4),large-scalefarmstendedtohaveaslowerincidenceofAHPND
thanmedium-andsmall-scalefarms.Thisshowedthatstratificationcouldminimisethe
self-selectionbiasintheresearch.Moreover,withpriorbusinessinThaishrimpfarming
andexperiencewithdiseaseproblems, thePhase1population shouldprovide good
representatives in Phase 2 in terms of providing information about the association
betweenAHPNDoccurrenceandriskfactors.
Chapter3Cross-sectionalstudy
112
Alltheidentifiedriskfactorsconveytheneedforchangesinfarmmanagementpractices
at shrimp farming sites. Other risk factors for AHPND may be included in further
researchandcross-sectionalstudies,however.Effectivestrategiestodevelopdisease
prevention and control at the country level are also needed. This can be achieved
throughanalysing the structureof the live shrimpmovementnetwork to gainmore
understanding of howdisease epizootic dynamics play out across thewhole shrimp
farmingsectorinThailand.
Chapter3Cross-sectionalstudy
113
3.7 References
Adhikari,S.,Lal,R.andSahu,B.C.(2012)CarbonsequestrationinthebottomsedimentsofaquaculturepondsofOrissa,India.EcologicalEngineering,47,pp.198-202.
Akaike,H.(1974)Anewlookatthestatisticalmodelidentification.AutomaticControl,IEEETransactionsOn,19(6),pp.716-723.
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Chapter 4 - Analysis of the network structure of the live shrimp movements relevant to AHPND epizootic
N.Saleetid;D.M.Green;F.J.Murray
Preface
The fourth chapter analyses the structure of the live shrimpmovement network of
Thailand(LSMN)usinggraphtheoryandnetworkapproaches.Theanalysisisaimedat
finding the real structure of the LSMN and to thereby suggest potential disease
transmission mechanisms, which is an important step towards the prevention and
control of a disease epizootic. The epizootiological survey conducted in Chapter 3
providesinformationondiseasemitigationmeasuresasanaidininterpretingresultsin
this chapter. The chapter is designed for publication, and thus, the spread of acute
hepatopancreatic necrosis disease (AHPND) is described again in the introduction
section.Thefirstpartdescribesthegeneralcharacteristicsof theLSMN.Second, the
network is visualised according to the provincial borders of Thailand. Then it is
quantified using statistical andmathematicalmeasures. The final part discusses the
network structure relevant to the spreadofAHPND.This chapter contributes to the
developmentofacontrolstrategyinthefifthchapterandepizooticmodelsofAHPND
inthesixthchapter.
Impactstatement
TheLSMNarewellrecorded,buthavebeenusedinalimitedextenttoassessthespread
ofdisease.To increaseunderstandingof the spreadofdisease fromsite to site, the
LSMNwasmodelledand its structureexamined in termsof relating these factors to
potential disease epizootics between sites. This is the first project to use network
modellingtocharacterisepotentialdiseasetransmissionintheshrimpfarmingsector
and provides authoritative information to the Thailand Department of Fisheries for
targeteddiseasesurveillanceandcontrolwithlimitedoperatingresources.
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Chapter 4 - Analysis of the network structure of the live shrimp movements relevant to AHPND epizootic
4.1 Abstract
This research models and analyses the live shrimp movement network of Thailand
(LSMN),whichhasapotentialeffectonsite-to-sitediseasetransmission.Themovement
data were collected over a 13-month period from March 2013 to March 2014.
Importantly,thespreadofacutehepatopancreaticnecrosisdisease(AHPND),andother
known diseases, occurred during the period covered. Results show that large-scale
connectivityintheLSMNtypicallyreliesoninter-provincemovementswithanaverage
distanceofaround200km.TheLSMNwasexaminedbynetworkmodellingandfound
to have a mixture of characteristics both hindering and aiding disease spread. For
hindering transmission, the correlation between 𝑖𝑛 and 𝑜𝑢𝑡degrees was weakly
positive,i.e.siteswithahighriskofcatchingdiseaseposedalowriskfortransmitting
the disease (assuming solely network spread), and the LSMN showed disassortative
mixingin𝑟(𝑖𝑛, 𝑜𝑢𝑡),i.e.alowpreferenceforconnectionsjoinsiteswithhigh𝑖𝑛degree
link toconnectionswithhigh𝑜𝑢𝑡degree.However, therewere lowvalues formean
shortestpathlengthandclusteringcoefficient.Theselattercharacteristicstendtobe
associated with the potential for disease epizootics. In addition to the small-world
property(i.e.shortmeanpathlength)presentedintheLSMN,thenetworkexhibited
power-law distributions of 𝑖𝑛 and 𝑜𝑢𝑡 degrees with exponents of 2.87 and 2.17,
respectively, indicating the scale-free phenomenon. This result showing the
heterogeneity in site degrees demonstrates that a targeted strategy can potentially
performwelltominimisethescaleofadiseaseepizootic.
4.2 Introduction
Shrimpfarminghasbeeninvolvedinthesocio-economicgrowthofThailandsincethe
1980s(Szuster,2006).Nevertheless,theinvasionofinfectiousdiseasespresentsasthe
majorbarriertosuccessinthesector.ThehistoricaldiseasesofAsianshrimpfarming
overthepast30yearshadbeenreviewedinFlegel(2012).Microparasites,i.e.viruses
andbacteria,areamajorcauseofAsianfarmedshrimpdeaths,representingarelatively
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huge economic loss (Flegel, 2012). This is a reasonwhy theWorldOrganisation for
AnimalHealth(OIE)requiresallmembercountriestodevelopdiseasesurveillanceand
controlmeasuresforaquaticanimals.
Intermsofpathwaysofshrimpdiseasetransmission, long-distancetransmission(i.e.
liveshrimpmovements)posesahighpotentialforsite-to-sitediseasespread.Totakea
recentexample,acutehepatopancreaticnecrosisdisease(AHPND,alsoknownasEMS)
hashitThaishrimpfarmingsincelate2011,andthediseasehasbeentransmittedacross
Thairegionsthroughthemovementsofinfectiousliveshrimp(FAO,2013b;OIE,2013a).
ThisepizooticdiseasedamagedThaishrimpproductionbyanestimated500000tonnes
of shrimpduring a 3-yearperiod from2011 to2014 (Songsanjinda, 2015).Although
there have been many worldwide efforts to stop the spread of AHPND, such as
movement restrictions, biofloc technology, genetics improvements, and enhanced
breeding techniques (Hong et al., 2016; Pakingking Jr et al., 2016), no such control
strategiesforAHPNDemergefromnetworkmodelling.
Network modelling is playing an increasingly important role in epizoology. Its
application relies on graph theory. A graph, or network, includes a set of sites (in
network terminology: nodes) and their connections. Most often, weights of
connections,suchasthefrequenciesofconnectionsbetweenthesamepairsofsitesare
ignored, by analysing non-weighted networks. There are two reasons for this: (1)
weightednetworksaremorecomplextoanalyseand(2)therehasbeenalackofoff-
the-shelf tools that can be used to analyse them, whereas many tools have been
designedfornon-weightednetworks(Newman,2004;Robinaughetal.,2016;Weiet
al., 2013; Yang et al., 2012). However, weighted networks generate an improved
representationandamore realistic structure thannon-weightedones (Barrat et al.,
2004;Wiedermannetal.,2013).Consequently, thereareagrowingnumberof tools
usedforweightednetworks.Thismeansthatthisstudycanevaluatetwoformsoflive
shrimpmovementnetwork:non-weightedandweightednetworks.
Networkmodel approaches can be helpful conveniently to plot andmathematically
describetheconnectionswithincontactnetworks,andtopredictdiseasedynamicsand
persistencefromtheirderivedstructure(Kurversetal.,2014;Meyersetal.,2003).Many
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studies of disease outbreaks have used both simulated and real networkmodels to
evaluateandcomparetheeffectivenessofcontrolstrategies(Caietal.,2014;Guinatet
al.,2016;Hartvigsenetal.,2007;Maetal.,2013;Werkmanetal.,2011).Huangetal.
(2016),however,suggestedthatrealnetworksreconstructedfromreal-lifeconnections
facilitatedmorereliablepredictionofdiseaseepidemicsthanusingsimulatednetworks.
Hence,therealnetworkmodel isbecoming increasingly important invarioussectors
suchasaquaculture(Greenetal.,2012;MunroandGregory,2009;Tayloretal.,2010),
livestockproduction(Aznaretal.,2011;Büttneretal.,2013;Kissetal.,2006),andplant
trade(Moslonka-Lefebvreetal.,2011).
The structure of the live shrimp movement network of Thailand (LSMN) has been
examined in this research, to explain its susceptibility to disease transmission. The
computer-based recordingof real live shrimpmovementsbetween sites servedas a
data source here, following from the aquatic animal trade regulation of Thailand,
B.E.2553(2010).Therecordingofmovementsisoperatedbyauthorisedusers,providing
uniquerecordsinthatthesourcesanddestinationsofdailyshrimpbatchmovements
can illustrate the spread of diseases from site to site. Furthermore, this research
indicatesthatmovementrecordshaveakeyroleinshrimpepizoology,contributingto
the enhancement of disease surveillance and control strategies in the Thai shrimp
farmingsector.
4.3 Methods
4.3.1 Datasources
Theliveshrimpmovementnetwork(LSMN)data—anelectronicandThaigovernment
database—wasprovidedbytheThailandDepartmentofFisheries.Itcontainsrecords
collectedfromdailybatchmovementsconsistingof:(1)thefarmregistrationnumber
(thefirsttwodigitsdenoteprovincialsitelocationasshowninAppendixCattheendof
thisthesis;inthethirdandfourthpositions,01denoteongrowingsiteand02denote
seed-producingsite),(2)thesourcesiteanddestinationsiteofliveshrimp,(3)thedate
ofmovement,and(4)theseedquantity.Importantly,thedataincludedthe13-month
periodofAHPNDspreadfromMarch2013toMarch2014duringwhichAHPNDspread
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aroundthecountry(NACA,2017),andcovereduptothreeproductioncyclesoffarmed
shrimp(Flahertyetal.,2000).Unfortunately,unreportedmovements,mostlygenerated
from non-commercial farming with low productivity and for breeding improvement
purposeswerenotavailable.Thisrepresentedalimitationofthestudy,althoughthe
expectednumbersaffectedareverysmall.
Microsoft Access was used to combine circa 99 000 available records, combining
multiplerecordsofabatchmovedwithinadayasoneconnection.Weomittedrecords
havingnorecordeddata inanyof the fields.Furthermore,167recordswerechosen
randomlyfromtheentiredatasetfordatavalidationintermsofthephysicallocationof
sourceanddestinationbecausefailuresinthisregardcouldaffectthereliabilityofthe
networkmodelling.Thissamplesizeofthe167recordswasobtainedfromtheonline
EpiToolsepidemiologicalcalculators(http://epitools.ausvet.com.au).Thenwechecked
thephysicallocationofsourceanddestinationinsuchchosenrecordsagainsttheThai
shrimp farm registration database obtained from Coastal Aquaculture Research and
DevelopmentDivision(2013),resultinginfourerrorsatvillagelevel(moredetailofthe
ThailocalgovernmentalunitswasgivenbyNagaietal.,2008).Thus,the95%confidence
intervalforthepopulationproportionofsucherrorswascalculatedas0.9%to6%,which
was computed by using the 𝑅 Programme Environment with the Hmisc (binconf)
package (Harrell, 2015; R foundation for statistical computing, 2015). These errors
woulddecreasereliabilityinourresultsifvillageswereusedtoidentifysitelocations,
butarenotimportantinthischapter.
4.3.2 Identificationofliveshrimpmovementtypesbyprovincialscale
UsingtheprovincialbordersofThailand,theliveshrimpmovementscouldbedividedinto
twotypes.Theuseofprovincialborderscorrespondswiththeprovincialadministration
of Thailand Department of Fisheries (www4.fisheries.go.th). Firstly, intra-province
movementisaconnectionbetweentwositeswithinthesameprovince;secondly,inter-
provincemovementisaconnectionbetweentwodifferentprovinces.Furthermore,the
distancebetweentwosites—𝑖and𝑗—wascomputedasstraight-linedistance(𝑑)basing
on the geographic coordinates of the Thai sub-districts available fromGoogle Earth
(2015).Theobtainedresultsrepresentedtheexpecteddistancesofinfectionbetween
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sitesasproposedinBogeletal.(1976).Theformulaforthestraight-linedistance(4.1)
showninDubéetal.(2008)is:
(4.1)
where𝑡isthecircumferenceoftheearth(=40075km).
4.3.3 Provincialvisualisationfortheliveshrimpmovementnetwork(LSMN)
Accordingtothetwomovementtypes(intra-andinter-provincemovements),theLSMN
provincialmovementsfor37provinceswerevisualisedbyusingPajekSoftware(Mrvar
and Batagelj, 1996). The frequency of connections for the intra-province and inter-
provincemovementswerepresentedwithdifferentlinecoloursandscaledlinewidths
onalogscaleduetothewidevariationinsizes.Inanefforttoillustratethemovement
networkbasedonnationaladministrativeboundaries,thepositionof37provinceswas
approximatedtothegeographiclocationsonamapofThailand(Kaesler,N.A.),using
Inkscapesoftware(www.inkscape.org).
4.3.4 LSMNadjacencymatrixfornetworkrepresentationandanalysisatsitelevel
Thequantitativeanalysisof theLSMNstructurewasperformed in the𝑅Programme
Environment(Rfoundationforstatisticalcomputing,2015).TheLSMNwasrepresented
byanadjacencymatrix.The igraph softwarepackage(CsardiandNepusz,2006)was
used since it is more flexible for analysing a large complex network with a sparse
adjacencymatrix (a squarematrixused to representanetworkwhoseelementsare
mostly zeros). The package provides many network analysis tools, e.g. matrix
eigenvalues,andnetworkrewiringalgorithmsusedinthisresearch(CsardiandNepusz,
2006).Inadditiontothe igraphpackage,wealsousedthetnetpackagefromOpsahl
(2009)tocalculateweightedshortestpathsandweightedclustering.
dij =t
360⇥ (x2 + y
2)12
x = (longitudesite j � longitudesite i)⇥ cos(latitudesite i)
y = latitudesite j � latitudesite i
Chapter4Networkanalysis
125
InthecaseoftheLSMN,theweightsoftheseconnectionsweretakenintoaccount.A
weight𝑤𝑖𝑗impliedthefrequencyofconnectionsthatvariedinsuchsitepairs(𝑖,𝑗).Thus,
the LSMNwas represented by the adjacencymatrixℎ,whichwas the element-wise
multiplicationofmatrix𝑎bymatrix𝑤asexplainedabove.𝑎𝑖𝑗tookthevalue0ifthere
wasnoconnectionfromsite𝑖tosite𝑗and𝑎𝑖𝑗tookthevalue1otherwise,representing
apathwayofdiseasetransmissionfrom𝑖to𝑗.About300self-loopconnections(𝑎>> =1)
wereremovedfromtheanalysisbecausetheseself-loopsdidnotcontributetofurther
spreadofdiseases(Brittonetal.,2011;Draiefetal.,2008).
Adirectednetworkcanbeusedtoquantifythelocalnetworkstructureofeachsiteby
calculating𝑖𝑛and𝑜𝑢𝑡degrees,andundirecteddegrees,asin(4.2)–(4.4),respectively.
𝐼𝑛degreesindicatethenumberofconnectionsmoveintoeachsiteand𝑜𝑢𝑡degrees
indicatethenumberofconnectionsmovefromeachsite.Undirecteddegreesdenote
thenumberofconnectionsineitherdirectionofeachsite.Thesenon-weighteddegree
calculations(norepeatedconnections)areshowninBarratetal.(2004)andGreenet
al.(2009).
(4.2)
(4.3)
(4.4)
Additionally,weightedmeasureswerecarriedoutforcomparisonproposes,asin(4.5)–
(4.7).
(4.5)
(4.6)
(4.7)
kini
=X
j
aji
kouti
=X
j
aij
kundi
= kini
+ kouti
�X
j
(aij
)(aji
)
kini
=X
j
aji
wji
kouti
=X
j
aij
wij
kundi
= kini
+ kouti
Chapter4Networkanalysis
126
Thetotalnumberofconnections(𝑀)wasequaltothetotalnumberofeither𝑖𝑛degrees
or𝑜𝑢𝑡degrees(4.8).
(4.8)
ThedegreedistributionsoftheLSMN(theweightednetwork)werestatisticallyanalysed
for a power-law characteristic and exponents were estimated, using the
Kolmogorov-Smirmovtestwiththeigraphsoftwarepackage(CsardiandNepusz,2006).
ItsimplementationusesthemethodofClausetetal.(2009)andNewman(2005);the
nullhypothesisisthattheLSMNisgeneratedfromapower-lawdistribution.Otherbasic
statisticsofthedegrees,i.e.summary,mean,maximumandminimum,andcoefficient
ofvariation,werealsopresentedintheresults.
Theshortestpath𝐿>? denotesaconnection𝑖 → 𝑗inanetwork.The𝐿>? fornon-weighted
directednetworkswascalculatedbyusingtheDijkstra'salgorithm(CsardiandNepusz,
2006; Dijkstra, 1959). Weighted directed networks require more computations,
however; for these, the 𝐿>? was computed as in Opsahl (2009): first the 𝐿>? was
calculatedbyusingtheDijkstra'salgorithm,thenitwasdividedbythemeanconnection
weightofthenetwork(thetotalnumberofrepeatedconnectionsdividedbythetotal
numberofconnections),givinganadjacencymatrixoftheweightedshortestpathsfor
theLSMN.ToexplainOpsahl’salgorithm,asmallnetworkisshowninFigure4.1.Noted
that,insomecases,theweightedshortestpathsdonothaveaclearmeaninginreallife
suchasgivingthedecimalvaluesofshortestpathlengths.
M =X
j
kini
=X
j
kouti
Chapter4Networkanalysis
127
(a) (b)
Figure 4.1 A small weighted directed network (a) and its matrix of the shortest paths 𝑳𝒊𝒋 (b) computed by the algorithm of Opsahl (2009). The average shortest path length ⟨𝑳⟩of this networkis1.84 (= 36.8/20).
Theaverageshortestpathlength⟨𝐿⟩wascomputedfollowingMaoandZhang(2013)
(4.9):
(4.9)
where[𝑋]istheIversonbracketdenoting1wherecondition𝑋istrueand0otherwise.
Clusteringreferstothepresenceofgroupedsitesthatthediseasecantransmitthrough
(ShirleyandRushton,2005).Thecharacteristicof clusteringwasdemonstratedby the
clusteringcoefficient(𝐶)derivedfromtheconceptof‘anyfriendofyoursisafriendof
mine’.ThiswassimilartothestudyofGreenetal.(2012).𝐶wascalculatedasaratioof
thenumberoftrianglestothenumberoftriples.AhnertandFink(2008)definedatriangle
asasetofthreesiteswherewith{𝑆o → 𝑆p → 𝑆q, 𝑆o → 𝑆q},meaningthatbothdirectand
indirect routes for𝑆o → 𝑆qexist,andwheresucha trianglecorrespondedtoa triple
S1 S2 S3 S4 S5S1 NA 2.4 1.2 3.0 3.2S2 0.6 NA 1.8 0.6 0.8S3 1.2 2.4 NA 3.0 3.2S4 1.2 0.6 2.4 NA 1.4S5 1.8 1.2 3.0 1.8 NA
1
4
3 24
4
112
2
1.Calculatethemeanedgeweight
24/10 2.4
2.Evaluatetheshortestpaths(Anexampleforasite‘ ’)=2.4/1=2.4=2.4/2=1.2=2.4/1+2.4/4=3.0=2.4/1+2.4/3=3.2
3.Givethematrixofshortestpaths
From
To
Chapter4Networkanalysis
128
𝑆o → 𝑆pand𝑆o → 𝑆q.Thus,the𝐶 istheproportionoftripleswhereadirectrouteof
transmissionalsoexists.
Wecomputed theassortativity coefficient (𝑟) to represent theassortativemixingby
degreeintheLSMN.Assortativemixingbydegreeiscommonincontactnetworksof
persons and animals. In addition to being a network property that aids disease
transmission, assortative mixing inhibits the effectiveness of targeted vaccination
strategiesduetothepersistenceofgiantcomponents(thelargestnumberofsitesina
network thatare interconnectedbydirectedconnections) in thenetwork (Newman,
2003). Theextentofassortativemixing shows the tendencyof sites inanetwork to
connecttoothersiteswithsimilardegrees,i.e.high-degreesitestendtobeconnected
to other high-degree sites (Newman, 2003). In the context of epizoology, this can
indicate thatdisease ismoreeasily spread in thenetwork, leading toahigherbasic
reproduction number (𝑅r) compared with a network that has a negative value of
assortativitycoefficient(disassortativity).Fosteretal.(2010)statethattheassortativity
of directed networks can be represented by four measures: 𝑟(𝑜𝑢𝑡, 𝑖𝑛), 𝑟(𝑖𝑛, 𝑜𝑢𝑡),
𝑟(𝑜𝑢𝑡, 𝑜𝑢𝑡),and𝑟(𝑖𝑛, 𝑖𝑛).Amongthesefourmeasures,however,themostinteresting
forepidemiologicalstudyisthe𝑟(𝑖𝑛, 𝑜𝑢𝑡)—i.e.directedconnectionsjoiningsiteswith
a high 𝑖𝑛 degree link to directed connections joining sites with high 𝑜𝑢𝑡 degree—
because thismeasure describes the patterns of network connections,which lead to
epidemics.ItsequationisshowninGreenetal.(2009),andiswrittenbelowby(4.10):
(4.10)
where𝑘>>8and𝑘?7s[denotethe𝑖𝑛degreeofsourceand𝑜𝑢𝑡degreeofdestinationfor
connection𝑖 → 𝑗respectively,and𝑀isthetotalnumberofconnectionsinthenetwork.
HeesterbeekandDietz(1996)definethebasicreproductionratio(𝑅r)astheexpected
numberofsecondarycasesgeneratedbyatypicalcaseduringitstransmissionperiodin
aparticulargroupofsusceptibleindividuals.Inordertoestimatetheepidemicthreshold
r(in, out) =M
Pi!j
k
in
i
k
out
j
�⇣P
i!j
k
in
i
⌘⇣Pi!j
k
out
j
⌘
rhM
Pi!j
(kini
)2 �⇣P
i!j
k
in
i
⌘2ihM
Pi!j
(koutj
)2 �⇣P
i!j
k
out
j
⌘2i
Chapter4Networkanalysis
129
basedonnetwork topology,oneestimate for𝑅r fromadegree-basedestimation is
giveninGreenetal.(2012)(4.11):
(4.11)
where⟨𝑋⟩representsanaverageofthe𝑋value.
Anestimated𝑅r >1indicatesthatanoutbreakcanspreadthoughthenetworkupon
introduction (Heesterbeek and Dietz, 1996; Jones, 2007). In addition, the largest
eigenvalueloftheLSMN’sadjacencymatrixℎwascalculated,sincethiscloselyrelates
to theepidemic threshold in thenetwork (BeckerandHall, 1996;Chakrabarti etal.,
2008; Prakash et al., 2010). With few closed cycles in the network, however, this
measure could easily be zero, or highly non-representative of the network. Thus, a
simple adjustment was made, following Green et al. (2009), i.e. adding a constant
number(𝐾=0.5)toallconnections,asin(4.12).
(4.12)
Then,thelargesteigenvaluelwascomputedwiththeeigen_centralityfunctioninthe
igraph package (Csardi andNepusz, 2006). This resultwas obtained by solving the
equation ℎ𝑉 = 𝑉l , where 𝑉 is called an eigenvector of ℎ corresponding to an
eigenvalue(Restrepoetal.,2007).
4.3.5 Rewiringthenetwork
Toevaluatethesmall-worldpropertyofthenetwork,astructuralcomparisonwasmade
fortheLSMN,foreither𝐶or⟨𝐿⟩,bycomparingitwithrandomlyrewirednetworks,while
preservingthenumberofsitesanddegreedistributionfromtheoriginalone(Evans,
2007; Kiss andGreen, 2008;Maslov and Sneppen, 2002;Noldus andVanMieghem,
2013). One thousand rewired networks were developed, where in such rewired
networks the probability of rewiring was set at one, resulting in all the two-pair
connectionsintheLSMNbeingswapped.Forexample,twoconnections𝑆o → 𝑆pand
𝑆q → 𝑆v are replacedby thesimulatedconnections𝑆o → 𝑆v and𝑆q → 𝑆p (seeFigure
R0 ⇠ hkinkoutiphkinihkouti
hadjustij =
Kwij
N+ hij
Chapter4Networkanalysis
130
4.2).TheMann-Whitney𝑈testwasusedtocomparefor⟨𝐿⟩intheoriginalnetworkwith
thedistributionofrewiredvalues.Theresultaddressedthequestionofwhetherthe
originalnetworkisconsistentwiththerewiredones.
Figure 4.2 An example of a rewiring process which generates a new network by swapping the endpoints of two-pair connections in a network.
Connectionscanberewiredlocallyornon-locally,resultinginchangingthesusceptibility
ofthenetworkto infection.Thus,theeffectoftherewiringprocessonthepotential
epizootic size was also evaluated here. The effect of the rewiring process on the
potential epizootic size in the 1 000 rewired LSMNswas assessed by themeasures
explainedbelow.
Sitereach.ThisnetworkmeasurewasproposedinGreenetal.(2012).Thenumber
ofsitesreachablefromanyother(𝑅>)inanadjacencymatrixofshortestpaths(𝐿>?)was
countedasin(4.13):
(4.13)
where[𝑋]istheIversonbracketagain.
Themaximumreachineachrewirednetworkwasusedtorepresentanestimateofthe
worst-caseepidemicsize,andthemeanreachimpliedatypicalepidemicsize(Greenet
al.,2012).Bothmaximumandmeanreacharedefinedin(4.14)and(4.15),respectively:
(4.14)
(4.15)
where𝑁isthetotalnumberofsitesinthenetwork.
Max reach = maxi(Ri)
Mean reach =
PRi
N
Chapter4Networkanalysis
131
Giantstronglyconnectedcomponents(GSCC)fordirectednetworks.AGSCCrefers
to the largest number of sites in a network that are interconnected by directed
connections.Thishasoftenbeenusedtoassessthepotentialepidemicsizeofnetworks
(Kaoetal.,2007;Kissetal.,2006;Rautureauetal.,2011).Danonetal.(2011)explained
thatallsitesinaGSCCwereabletocatchthediseaseiftheinfectionwasseededbyany
ofthosesites.Inotherwords,allsitesintheGSCCwereequallyatriskfromtransmission
(Greenetal.,2009;Yatabeetal.,2015).Inadditiontostronglyconnectedcomponents,
all connectionswere also considered as bidirectional in order tomeasure the giant
weaklyconnectedcomponentorGWCC(thelargestnumberofsitesinanetworkthat
areinterconnectedbyundirectedconnections)(Pastor-Satorrasetal.,2015).
TheGSCCwassoughtbytwoconsecutivedepth-firstsearchesbasedonthemethodof
Tarjan(1972).TheGWCC,meanwhile,wassearchedbyasimplebreadth-firstalgorithm
(Korf, 1985). Both implementations were run on the 𝑅 Programme Environment
packageigraph(CsardiandNepusz,2006;Rfoundationforstatisticalcomputing,2015).
To provide a better understanding of the possible outcomes, examples of the
implementationofthealgorithmsaregiveninFigure4.3(forSCC)andFigure4.4(for
WCC).
Figure 4.3 Strongly connected component of a directed network with eight sites. This small network has five strongly connected components (SCCs), which are shown by grey shading. The size of the giant strongly connected component (GSCC) is equal to three and contains sites 𝑺𝟓, 𝑺𝟔 and 𝑺𝟕.
Chapter4Networkanalysis
132
Figure 4.4 Weakly connected component of a bidirectional network with eight sites. This small network has two weakly connected components (WCCs), which are shown by grey shading. The size of the giant weakly connected component (WSCC) is equal to four, with a tie between the two sets of sites {𝑺𝟏, 𝑺𝟐, 𝑺𝟑, 𝑺𝟒} and {𝑺𝟓, 𝑺𝟔, 𝑺𝟕, 𝑺𝟖}.
4.4 Results
Thenetworkmodellingreportedinthissectionconsiderstheliveshrimpmovementdata
overa13-monthstudyperiodfromMarch2013toMarch2014.
4.4.1 GeneralcharacteristicsoftheliveshrimpmovementnetworkofThailand(LSMN)
4.4.1.1Thenumberofsites
Figure4.5displaysthegeneralcharacteristicsoftheLSMN.Circa13800shrimpfarming
siteswere located in37provincesof five regions, i.e. south (5665sites;41%ofthe
total),east(4874sites;35%),central(1949sites;14%),west(1312sites;9%),andone
site in the northeast. The highest number of seed-producing sites denoted both
hatcheriesandnurserieswasintheeasternregion(379sites;47%ofthetotal804seed
producingsites).Whereas11provinceshavenoseed-producingsites.Therangeand
mean of the ratio of seed producing sites to ongrowing siteswas 0–1.22 and 0.09,
respectively.
Chapter4Networkanalysis
133
Figure 4.5 Circa 13 800 shrimp farming sites located in five regions and 37 provinces of Thailand. Values in brackets show the number of seed-producing sites and ongrowing sites, respectively. Among regions, the highest number of sites is in south ( ) and the lowest number is in northeast ( ). These data were collected from the live shrimp movements in Thailand from March 2013 to March 2014.
4.4.1.2Thecharacteristicsofliveshrimpmovements
The diagrammatic representation of LSMN, which demonstrates the Thai shrimp
farmingindustrystructure,isshowninFigure4.6.Afewhatcheriesobtainbroodstock
L.vannameifromongrowingsiteswithbroodstockimprovementprogrammes.Instead
ofdirectsellingtheshrimpseedatPL10totheongrowingsites,thehatcheriespass
someoftheirproductiontothenurseriesatnaupliusstage.Then,manynurseriesrear
theseedfromthenaupliusuntilPL10beforesellingtheproductiontotheongrowing
sites.Thefigurealsoindicatesthatthereareafewnumberofshrimpseed(PL12–14)
movementsbetweentheon-growingsitesasthesesitesareconductedbyrelativesof
family.
Chapter4Networkanalysis
134
Figure 4.6 Diagrammatic representation of LSMN demonstrating the Thai shrimp farming industry structure.
Thecharacteristicsof liveshrimpmovementsaresummarised inFigures4.7and4.8.
Overall,circa13800siteswereinvolvedin33720site-to-sitemovements.Asshownin
Figure 4.7, thesemovements contained 74 462 repeated connections that included
57 281 connections entailing inter-provincemovements (77%of the total repeated
connections),andthemonthlymaximumwasreachedatabout6000connectionsin
March2014.Theremainingconnectionswereintra-provincemovements(23%),with
themaximumforthesebeingreachedatabout2000connectionsinSeptember2013.
Hatcherysite2
Nurserysite
Ongrowing site PL12-14
Hatcherysite1
Chapter4Networkanalysis
135
(a)
(b) (c)
Figure 4.7 Distribution of the number of repeated connections over the 13-month study period (March 2013–March 2014) of live shrimp movements in Thailand. (a) The total number of connections are stratified by two movement types: inter- and intra-province movements. (b) The monthly distribution by inter-province movements. (c) The monthly distribution by intra-province movements.
Figure4.8givesthenumberofshrimpmoved.Mostshrimpweremovedbetweensites
viatheinter-provincemovements(83%ofthe161133×10�shrimp).Themaximum
wasreachedatabout13000billionshrimpinMarch2014.Theremainingshrimpwere
movedbytheintra-provincemovements,countingaround17%,withthemaximumwas
reachedatabout3000billionshrimpinJanuary2014.
The results suggest that the movements, whether counted by the number of
connectionsorthenumberofshrimpmoved,cancontributetothespreadofdiseases
across provinces. Importantly, the provincial controls of inter- and intra-province
Num
bero
fcon
nections
10000
20000
30000
40000
50000
60000
Inter-provincemovements
Intra-provincemovements
57281(77%)
17181(23%)
0
100020003000400050006000
MAR DEC2013
JAN MAR2014
0Num
bero
fcon
nections
Maximum(MAR2014)
100020003000400050006000
MAR DEC2013
JAN MAR2014
Num
bero
fcon
nections
Maximum(SEP2013)
0
Chapter4Networkanalysis
136
movements (i.e. movement restriction) have the potential for preventing and
controllingdiseasespreadinThaishrimpfarming.
(a)
(b) (c)
Figure 4.8 Distribution of the number of shrimp moved over the 13-month study period (March 2013–March 2014) of live shrimp movements in Thailand. (a) The total number of shrimp moved were stratified by two movement types: inter- and intra-province movements. (b) The monthly distribution by inter-province movements. (c) The monthly distribution by intra-province movements.
AsmeasuredgeographicallyontheThaimap,themeanstraight-linedistancesofthe
intra- and inter-province movements were 24 and 192 km, respectively. The 75th
percentile of straight-line distance (km) for the inter-province movements was
approximately 200 km, and around 30 km for the intra-provincemovements. These
characteristicsofliveshrimpmovementscanbeusedtodescribediseaseepizooticsdue
tolong-distancetransmission.
0Num
bero
fshrim
pmoved
(
)
Inter-provincemovements
Intra-provincemovements
20000400006000080000100000120000140000 133990(83%)
27143(17%)
2000400060008000100001200014000
Jan-00
Jan-00
Jan-00
Jan-00
Jan-00
Jan-00
Jan-00
Jan-00
Jan-00
Jan-00
Jan-00
Jan-00
Jan-00
Num
bero
fshrim
pmoved
(
)
MAR DEC2013
JAN MAR2014
Maximum(MAR2014)
02000400060008000100001200014000
1 2 3 4 5 6 7 8 9 10 11 12 13
Num
bero
fshrim
pmoved
(
)
MAR DEC2013
JAN MAR2014
Maximum(JAN2014)
0
Chapter4Networkanalysis
137
4.4.2 VisualisingtheLSMNbasedonnationalprovincialcentres
TheLSMNvisualisationbasedonnationalprovincialcentresispresentedinFigure4.9.
The figure also displays a subset of an example of provincial dataset, which is the
movementsofliveshrimpfromallthreehatcheriesinSamutprakan(SPK)toongrowing
sites,asshowninthebox.Thelinesandarrowsinthefigureindicatetheconnectionsof
liveshrimpbetweensourcesitesanddestinationsiteswithfrequenciesofconnections
corresponding to the colour and width (on a log scale) of the line. For disease
surveillanceandcontrolintheThaishrimpfarming,theprovincialcontrolofmovements
haspotential in termsofpracticalitybecauseThailandhas localDoFoffices inall77
provinces, which play important roles in disease prevention and control
implementation.Thiscanbesupportedbyabetterunderstandingoftwomaintypesof
themovements(inter-andintra-provincemovements).
In Figure 4.9, The inter-province movements with the highest number of repeated
connections (> 1 500 connections) are displayed in the orange and thicker lines,
correspondingtoChonburi(CBI)→Chanthaburi(CTI),Chonburi(CBI)→Chachoengsao
(CCO),Chachoengsao(CCO)→Chanthaburi(CTI),Trat(TRT)→Chanthaburi(CTI),and
Chumphon(CPN)→Suratthani(SNI).Usingthesamekey,theintra-provincemovements
(i.e.aloopfromaprovincetoitself)withthehighestnumberofconnectionswerefound
inChachoengsao (CCO),Nakhonsithammarat (NRT),Phuket (PKT),Songkhla (SKA),and
Prachuapkhirikhan(PKN).
The visualised and quantitative outcomes of the LSMN bolsters understanding of
diseasetransmissionvialong-distancetransmissionintheThaishrimpfarmingsector,
andprovideinformationfortheplanninganddesigningofbasicdiseasesurveillanceand
controlmeasures.Forexample,inthecontextofnetworkmodels,provinceswithahigh
numberofconnectionsoutoftheprovincialboundariesmayhaveanimportantrolein
transmittingdiseasestootherprovinces.Hence,regulatorscanusetheresultsofthis
worktoallocatealargeramountofsurveillanceresourcestothoseprovinces,suchas
toChonburi(CBI)andTrat(TRT).
Chapter4Networkanalysis
138
Figure 4.9 The provincial structure of the live shrimp movement network of Thailand (LSMN) over a 13-month period (March 2013–March 2014). The shrimp farming sites are located in 37 provinces of Thailand. The line width is plotted on a log scale according to the actual number of repeated connections. Four classifications of connections are shown by the different line colours: (1) 1–500 connections with grey, (2) 501–1 000 connections with dark grey, (3) 1 001–1 500 connections in blue, and (4) > 1 500 connections in orange. The figure also displays a subset of an example of provincial dataset, which is the movements of live shrimp from all three hatcheries in Samutprakan (SPK) to ongrowing sites, as shown in the box. The abbreviation list of national provincial centres is shown in Appendix C.
NumberofrepeatedconnectionsSNI
1 501 1001 >1500
Liveshrimpmovementsfromthreehatcheries(H)inSPKto
ongrowing sites
Pajek
H H
H
Chapter4Networkanalysis
139
4.4.3 Descriptiveanalysisoftheliveshrimpmovementnetwork(LSMN)atsitelevel
ThesitedegreesoftheLSMNweremeasured,andtheresultsaresummarisedinTable
4.1.Itappearsthattherearedifferencesbetweenusingthenon-weighteddegrees(not
including repeated connections) and the weighted degrees (including repeated
connections).
ComputingtheLSMNasnon-weighted,themean(andcoefficientofvariation)forthe
𝑖𝑛degree𝑘>8was2.4(0.9)and2.4(9.2)forthe𝑜𝑢𝑡degree𝑘7s[.Theundirecteddegree
𝑘s8K was4.9(4.6).𝐼𝑛degree𝑘>8hadanarrowrangeofdegreesbetween0and30.In
contrast, 𝑘7s[had a larger range between 0 and 932. Using the Pearson product-
momentcorrelation,thecorrelationbetweenthesitedegrees𝑘>8and𝑘7s[wasweakly
positivewithavalueof0.03(𝑁=13801andP-value<0.01).Theweakpositivevalue
ofdegreecorrelationindicatedthatsiteswithahighriskofcatchingdiseaseposeda
low risk for transmitting the disease (assuming solely network spread). This also
reflectedthefragmentednatureofthenetworkforhinderingdiseasetransmission(Kiss
etal.,2006).
Examining the sitedegrees from theweightednetwork, thedegreepropertieswere
abouttwotimes largerthanforthenon-weightedone.Themean(andcoefficientof
variation)for𝑘>8was5.4(1.7)andfor𝑘7s[was5.4(13.4).Thevaluefor𝑘s8K was10.8
(6.9).𝐼𝑛degree𝑘>8hadanarrowrangeofdegreesbetween0and232. Incontrast,
𝑘7s[hadalargerrangebetween0and5839.Thecorrelationbetweenthesitedegrees
𝑘>8and𝑘7s[wasweaklypositivewithavalueof0.24(𝑁=13801andP-value<0.01).
The fact thatamuchweakercorrelationbetweenthesitedegrees𝑘>8and𝑘7s[was
observed with the non-weighted LSMN than with the weighted one suggests that
studyingthenon-weightednetworkwouldmissthiscorrelation,anditwouldleadto
underestimationofepizooticspread.
Chapter4Networkanalysis
140
Table 4.1 Degree properties of the live shrimp movement network of Thailand (LSMN). With two types of site degree calculations (non-weighted and weighted). The properties measured are total number of sites, total degrees, mean degrees, variation coefficient of degrees, and degree correlation.
Property Non-weighteddegree Weighteddegree
Totalnumberofsites(𝑁) 13801
Totaldegrees
- 𝑖𝑛degree
- 𝑜𝑢𝑡degree
- 𝑢𝑛𝑑𝑖𝑟𝑒𝑐𝑡𝑒𝑑degree
33720
33720
67414
74462
74462
148924
Meandegree
- 𝑖𝑛degree
- 𝑜𝑢𝑡degree
- 𝑢𝑛𝑑𝑖𝑟𝑒𝑐𝑡𝑒𝑑degree
2.4
2.4
4.9
5.4
5.4
10.8
Coefficientofvariation
- 𝑖𝑛degree
- 𝑜𝑢𝑡degree
- 𝑢𝑛𝑑𝑖𝑟𝑒𝑐𝑡𝑒𝑑degree
0.9
9.2
4.6
1.7
13.4
6.9
Degreecorrelation 0.03 0.24
Consideringweighteddegreedistribution,itwasfoundthattheLSMNdemonstrateda
power-law𝑃(𝑘)~𝑘/0forboth𝑖𝑛and𝑜𝑢𝑡degreedistributionswiththeexponents(𝛾)
2.87and2.17,respectively(Figure4.10).Theexponentsofsitedegreedistributionsin
theLSMNwereintherangeoftwotothree,similartomanyscale-freenetworks,as
proposedinGohetal.(2002).Additionally,theKolmogorov-Smirmovtestacceptedthe
power-law as a plausible model with large P-values>0.05 (P-values of 𝑘>8 = 0.7
and𝑘7s[=0.54).TheseresultsindicatethattheLSMNdisplaysascale-freetopology.
Ascale-freepropertyisofmajorinterestforepizoology(Bogunáetal.,2003;Shirleyand
Rushton,2005).InrespecttotheLSMN,thescale-freepropertyindicatedthatmostsites
hadverylowdegreesofconnectionsandfewsiteshadhighdegreesofconnections.The
transmission capacityof this small group couldbe comparedwith the80/20 rule as
Chapter4Networkanalysis
141
proposed in Woolhouse et al. (1997). These authors suggested that, given the
heterogeneityinsitedegrees,infectionin20%ofthetotalsitesissufficienttoleadto
theinfectionoftheremainingsites.
(a) (b)
𝒊𝒏degrees(logscale) 𝒐𝒖𝒕degrees(logscale)
Figure 4.10 The weighted degree distributions for the LSMN plotted on a log–log scale. The sites with higher 𝒊𝒏 degrees have a greater chance of being infected (a), and the sites with higher 𝒐𝒖𝒕 degrees have a greater risk for transmitting disease (b).
BasedontheweightedLSMN,the𝑅restimatedbythedegree-basedcalculation,was
high (~ 34.5), compared to the largest eigenvalue= 16.2. Thehigh valueof𝑅r can
indicate that the connectivity of the LSMNobeys a bipartite structure: the network
consists of two types of sites, i.e. seed-producing sites and ongrowing sites. To aid
understandingofthebipartitestructure,Table4.2showsthedescriptionofthetotal
numberofconnectionsbetweentwosite types in theweightedLSMN.Thebipartite
structureisalsoevidentinthenon-weightedLSMN(Table4.3).Fromthesetables,both
the non-weighted and weighted LSMN demonstrate a high number of connections
between different site types (> 80 % of the total connections). Nevertheless, the
remainingconnectionsjoinsiteswithinthesametypeofsite.Sinceasmallnumberof
connectionsdisobeyingsuchthisbipartitestructure,thismaygiverisetosomedifficulty
in interpreting𝑅r and indescribingwhat𝑅rmeans toseed-producingorongrowing
sites.
Freq
uency(lo
gscale) 𝑃>8~𝑘/p.�� 𝑃7s[~𝑘/p.o�
Chapter4Networkanalysis
142
Table 4.2 Description of the number of connections between seed-producing sites and ongrowing sites based on the weighted degree of the LSMN. Their proportions are given in brackets.
Weighteddegree Destination
Seed-producingsite Ongrowingsite
SourceSeed-producingsite 10775 (14.5%) 63596 (85.4%)
Ongrowingsite 7 (0%) 84 (0.1%)
Table 4.3 Description of the number of connections between seed-producing sites and ongrowing sites based on the non-weighted degree of the LSMN. Their proportions are given in brackets.
Non-weighteddegree Destination
Seed-producingsite Ongrowingsite
SourceSeed-producingsite 2047 (6.1%) 31589 (93.7%)
Ongrowingsite 7 (0%) 77 (0.2%)
Asmeasuredbytheweighteddegree,theLSMNshowed𝑖𝑛-𝑜𝑢𝑡disassortativemixing
withavalueof-0.09.ThisimpliesalowpreferenceforconnectionsintheLSMNthatjoin
siteswith ahigh 𝑖𝑛 degree link to thosewith ahigh𝑜𝑢𝑡 degree. This results in less
extensivetransmissionofthediseasetoothersites.Thisfindingalsoconfirmsthatthe
LSMNisahierarchicalnetwork(e.g.afewsitesaresourcesformanyconnectionsinthe
LSMN),similartowhathasbeenobservedinrelationtopigproductionintheUnited
States (Lee et al., 2017). According to Barthélemy et al. (2004), these few sites can
becomesuperspreadersfortransmittingdiseasesandinducefastepidemics.Inaddition,
usingthesameassortativitymeasure,theresultwassimilar intherewirednetworks.
This result demonstrates that the dynamics of connections do not affect assortative
mixingpatternintheLSMN.
Akeypropertyofsmall-worldnetworks(i.e.smallmeanpathlengths)isshowninboth
the non-weighted andweighted LSMN. Focusing on the non-weighted network, the
valueofthemeanpathlength 𝐿 wassmall(3.47),with0.5%ofpotentialtotalpaths
Chapter4Networkanalysis
143
𝑁(𝑁 − 1).FortheweightedLSMN, 𝐿 wasequalto2.99,with0.14%ofpotentialtotal
paths.Thesmallervalueof 𝐿 intheweightednetworkprovidesagoodexplanationfor
the speed of disease transmission in the network. For example, if the connections
betweentwosites(𝑖, 𝑗)aremorefrequent,asmeasuredbytheweightednetwork,a
diseasemight be transmitted quicker through this network structure than the non-
weightedone(Opsahl,2009),andthisisreflectedinthelowvalueof𝐿>?.
Tofurtherinvestigatetheemergenceofthesmall-worldproperty,thedistributionof
weighted path lengths is plotted in Figure 4.11. This clearly demonstrates that the
networkincludesmanyshortpathsandfewlongpaths.Inaddition,theresultsof 𝐿
werecomparedwiththesmall-worldexperimentofMilgrametal.(1992)whostudied
the‘sixdegreesofseparation’theory.Thissuggeststhatsitescangetadiseaseviaa
connection of no more than six intermediates, showing a small-world network. In
contrastwiththesmall-worldnetworksstudiedinWattsandStrogatz(1998),theLSMN
characterised a small 𝐿 but with low clustering as measured by a non-weighted
clusteringcoefficient𝐶=0.0051(theweighted𝐶=0.1).Thislowvalueof𝐶impliesthat
therearefewstrongties(triangles)intheLSMN.
Figure 4.11 The distribution of weighted path lengths in the live shrimp movement network of Thailand (LSMN) is shown as a fraction of total connections.
Pathlength
Prop
ortio
n
0.0020
0.0015
0.0010
0.0005
0
1⎼10
11⎼20
21⎼50
51⎼100
101⎼200
201⎼500
501⎼10
00
1001⎼2
000
2001⎼5000
5001⎼7290
Chapter4Networkanalysis
144
To provide more evidence in respect to this small-world property, the important
propertiesoftheLSMNcomparedwithrewirednetworksaresummarisedinTable4.4.
Usingthenon-parametricMann–Whitney𝑈testcomparingthemeanpathlength 𝐿
for theoriginalnetworkandrewiredones,weconcludedthat the 𝐿 of theoriginal
networkandtherewiredonesdidnotdifferata0.05significancelevel(P-value=0.84).
For1000randomlyrewirednetworkswithrewiringprobabilities=1 (conservingthe
originalLSMN’sweighteddegreedistributionandthenumberofsites𝑁),theaverage
clustering coefficient 𝐶was 0.06 (SD=0.008), and the average of 𝐿 was 3.84
(SD=2.12),with0.5%ofpotentialpathsexisting.These indicate that theclustering
coefficient(𝐶),andtheaverageshortestpathlength 𝐿 withintheLSMNarecloseto
valuescomputedfromthecorrespondingrandomlyrewirednetworks.Thus,eveninthe
eventoftheconnectionsbeingchanged,theLSMNstillhasapropertyofasmall-world
networkandasmallvalueofclusteringcoefficient.
Both properties of the LSMN were also compared with two networks of animal
movements.ThefirstwasthenetworkstructureofScottishlivefishmovements(561
sitesand1340connections)thathad𝐶=0.07and 𝐿 =5.92(Greenetal.,2012).The
secondwasthestaticnetworkofpigmovementsinGermany(97980sitesand315333
connections)thathad 𝐿 =5.5(Lentzetal.,2016).Asmallcharacteristicpathlength
andalowclusteringcoefficientdenotethatsite-to-sitetransmissionofdiseaseinthe
LSMNismucheasierandquickerthaninthesetwoalternativeexamples.
Table4.4alsoshowsthattheestimatedmaximumpotentialepizooticsizeintheLSMN
was smaller than in the 1 000 rewired networks. This result was assessed by two
networkmeasures: (1) site reach, including repeated connections, and (2) the giant
strongly connected component (GSCC) and the giant weakly connected component
(GWCC).
Withtheworst-caseepizootics,theaveragemaximumreachforallfullyrewiredLSMNs
washigherthanintheoriginalLSMN,anincreasefrom7290to11000(SD=449.7).As
measuredbymeanreach,theestimatedtypicalepizooticsizealsoincreasedfrom19.5
to72.5(SD=20.2).Similarly,themeansizeofGSCCwasgreaterfortherewiredLSMNs
comparedwiththeoriginalone,anincreasefrom5to27.4(SD=17.8).Incontrast,there
Chapter4Networkanalysis
145
wasnodifferenceinthesizeofGWCCbetweentheoriginalnetworkandtherewired
LSMNs.
Table 4.4 Estimated maximum and mean reach, size of giant strongly connected components (GSCCs), and size of giant weakly connected component (GWCCs) for both the LSMN and the rewired LSMNs
Networkmeasure LSMNFullyrewiredLSMNs
Average(SD)
Averagepathlength 𝐿 2.99 3.84(2.12)
Clusteringcoefficient𝐶 0.1 0.06(0.008)
Totalreach 268000 1005000(284000)
Maximumreach 7290 11000(450)
Meanreach 19.5 72.5(20.2)
SizeofGSCC 5 27.4(17.8)
SizeofGWCC 13000 13000(3.5)
4.5 Discussion
Many infectious diseases transmit among populations via network spread, such as
denguediseaseinhumans(Reineretal.,2014;Stoddardetal.,2013),foot-and-mouth
disease in domestic and wild animals (Sobrino and Domingo, 2017), and pancreas
disease in farmed fish (Stene et al., 2014). Thus, using graph theory and a network
approach,thestructureoftheliveshrimpmovementnetworkofThailand(LSMN)over
the13-monthstudyperiodwasinvestigatedinrespecttothepotentialspreadofshrimp
diseases,andtheimportantepizootiologicalpropertiesbothaidingandhinderingthe
spreadofdiseaseswerecharacterised.Becausethemovementsof liveshrimpplaya
crucialroleininfectiousdiseasetransmissionfromsitetosite,particularlyinrespectto
therecentoutbreakofAHPND(OIE,2013)andotherknownshrimpdiseases(Lightner,
1983),theseresultsareasteptowardsdesigninganeffectivediseasesurveillanceand
controlprogrammeforThaishrimpfarming.
Chapter4Networkanalysis
146
Networkvisualisationprovidesagoodsourceforstudyingdiseasespreadwithinreal
complexnetworks.VisualisingH1N1 influenzapandemicworldwide (Brockmannand
Helbing, 2013), for example, gives a better understanding of influenza spread via
personswhotravelledacrossc.4000airportsin2009.TheLSMNasvisualisedusingthe
37provincialborders,givesagood illustrationof theconnections in theThaishrimp
farming.Visualising the LSMNas adirectmovementnetwork, it canbe clearly seen
whichprovinceshavehighconnectionsinandout.Thenodesdenotinghatcherieshave
highriskofbeingsourceof infection; thenodesdenotingnurserieshavehighriskof
beingsourceandsinkofinfection;thenodesdenotingongrowingsiteshavehighriskof
being sinkof infection.Additionally, theprovincial visualisationwith twomovement
types (intra- and inter-provincemovements) emphasises that the regulators should
increase efforts in respect to movement controls even during normal ‘peacetime’
situation.
TheresultsalsodemonstratethesepropertiesoftheLSMNthateitherhinderoraidthe
spread of disease. For hindering the transmission, the LSMN had weak correlation
betweensitedegrees.Foraidingtransmission,theLSMNdisplayedasmallcharacteristic
pathlengthandlowclustering.Itshouldbenotedthatclusteringisaverylocalclustering
measurement,asmeasuredbytheclusteringcoefficient.Notallclusteringcoefficients
arenecessaryallthatlocal,whilethereislotsofclusteringatlargescalesduetolocal
tradingbeingcommon intheLSMN(e.g.withinprovince).Thus,whentheclustering
wasremovedbyrewiring, itwasnotsurprisingthattheestimatedpotentialepizooic
sizealsoincreased.
For Thai shrimp farming, repeated connections between sites are common because
therearea few shrimp seedproducers inThailand. Thepatternof connections that
often repeat may relate to the rapid transmission of AHPND and other infectious
diseases in thenetwork.Asmeasuredbymeanpath length 𝐿 , theweighted LSMN
(includingrepeatedconnections)displayedashortervalueof 𝐿 thanthenon-weighted
one (not including repeated connections). In this case, Shirley and Rushton (2005)
describethatanetworkwiththeshortestvalueof 𝐿 ismorelikelytohavethefastest
rateofinfectionduringperiodsofepizootic.Fastdecisionmakingisthereforerequired
Chapter4Networkanalysis
147
topreventdiseaseepizooticsintheThaishrimpfarming,inparticularwhenthenumber
ofrepeatedconnectionsincreases.
The structure of the LSMN is scale-free (exhibiting power-law 𝑖𝑛-and 𝑜𝑢𝑡-degree
distributions with the exponents𝛾 = 2.87 and 2.17, respectively). These results
correspondedtothestudiesofPastor-SatorrasandVespignani(2002).Theseauthors
demonstratedthatthevariancesofpower-lawdistributions0< 𝛾 ≤3becameinfinite,
resultinginzeroepidemicthresholdsanddiseasespreadandpersistenceinscale-free
networks at any transmission rate, while exponents above three indicated finite
variances.ChatterjeeandDurrett(2009)alsofoundanon-zeroepidemicthresholdin
random networks with power-law distributions 𝛾 >3. A disease-control strategy
focusedon reducing the transmissionprobabilitywouldprobably apply to the cases
where𝛾 >3(Newman,2002).Topreventdiseasespreadinsuchpower-lawnetworks,
therefore,controlstrategiesfocusedonkeepingnon-zeroepidemicthreshold,suchas
by targeting highly connected sites, aremore effective (Dezső and Barabási, 2002).
Nevertheless,thesearetheoreticalconsiderations.Anyactualdatasethasafinitemean
andvariance.
TheimportantpropertiesofLSMNthatinfluenceonthespreadofdiseasesaredetected
bymodellingthe1000rewirednetworksinordertoexaminesmall-worldphenomenon
and estimate epizootic sizes. In these rewired networks, themean path length 𝐿 ,
reachability,andgiantstronglyconnectedcomponents(GSCC)tendedtobelargerthan
intheoriginalnetwork,whiletheclusteringcoefficient(𝐶)becamesmaller.Inaddition,
theassortativemixingremainedconstantthroughoutalltherewirednetworks.Among
these properties, the increases in either the maximum reach or giant connected
components afternetwork rewiring canbeexplained through thebehaviourof Thai
shrimpfarmersthatwasobservedduringtheface-to-faceinterviewsintheChapter3
“EvaluatingriskfactorsforAHPNDtransmissionintheThaishrimpfarmingsector”.The
ongrowingfarmersarelikelytomakeanewcontactwhentheyfeeldissatisfiedwiththe
seedquality.Consequently,thisbehaviourcontributestoanincreaseintheestimated
epizooticsizeintheThaishrimpfarming.
Chapter4Networkanalysis
148
TheLSMNthatwestudyheredoesnotcontainbothunreportedmovementsandabout
300self-loopconnections.Thisisanacknowledgedlimitationofthestudy.Unreported
movementsaretypicallygeneratedfromnon-commercialfarmingwithlowproductivity
andforbreedingimprovementpurposes,whereasself-loopsarecausedbysitesthatact
as both seed-producing and ongrowing sites, which hold same farm registration
number.ThestructureoftheLSMNmaychangeifthenetworkanalysisincludesdata
setinrespecttounreportedmovementsandself-loops.
Themovementsbetweenseedproducingsitesrepresentedthemovementsofshrimp
seedwithnaupliusorinitialPLstagesfromhatcheriestonurseries.Thedatacovereda
fewmovementsfromongrowingsitestoseedproducingsites(hatcheries)forbreeding
improvement purposes. The movements between ongrowing sites appeared to be
sharingshrimpseedbetweenrelativesoffamilysuchasfather’sandson’sfarms,for
example.
Insummary,ournetworkanalysisdescribesthescopeofpotentialdiseasetransmission
among the Thai shrimp farming sites via live shrimp movements. The LSMN is
characterisedbyimportantepizootiologicalpropertiesthatbothaidandhinderdisease
transmission.Becausescale-freepropertiesarefoundintheLSMN,weemphasisethat
optimaltargeteddiseasesurveillanceandcontrolcanreducethespreadofepizooticsin
the Thai shrimp farming. Moreover, not only can a targeted strategy offer more
effectivepreventionofdiseaseepizootics,butitalsoprovidesamoreefficientuseof
limitedresourcesfordiseasesurveillanceandcontrol.Theseconsiderationsleadusto
furtherresearchinthenextchapter.
Chapter4Networkanalysis
149
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Chapter 5 - Target priority for targeted disease surveillance and control in the live shrimp movement network of Thailand
N.Saleetid;D.M.Green;F.J.Murray
Preface
Chapter5usesthedataintroducedinChapter4.Theaimofthischapteristodevelopa
targeted disease-control algorithm for the Thai shrimp farming sector based on the
analysisofthestructureoftheliveshrimpmovementnetwork(LSMN)undertakenin
Chapter 4. This chapter is designed for publication; thus, in its introduction part
informationonthespreadofAHPNDinshrimpfarmingsitesisgivenagain.Oneofthe
keystoworkingsuccessfullywiththealgorithmiswriting𝑅scripts.These𝑅scriptswere
developedbyprogrammingworkdonebytheauthor,butwithadvicefromD.M.Green,
andaredifferentfromthoseinpreviousstudies.
Impactstatement
Diseasesurveillanceandcontrolcanleadtoreducedpopulationlossesfrominfectious
diseasesbydetectingandtreatingepizooticsatanearlystage.InthecaseofThaishrimp
farming,unlesstargeteddiseasesurveillanceandcontrolisestablished,diseasescould
spread and persist among sites, due to the structure of the LSMN. Thus, this study
adoptednetworkapproachestoidentifyhigh-riskconnectionswhoseremovalfromthe
network could reduce epizootics. The targeted disease-control algorithms obtained
fromthisstudyprovidetheThailandDepartmentofFisherieswithagoodstrategyfor
planningtheannualdiseasesurveillanceandcontrolprogrammeforfarmedshrimpand
otheraquaticspecies.
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Chapter 5 - Target priority for targeted disease surveillance in the live shrimp movement network of Thailand
5.1 Abstract
Targeteddiseasesurveillanceandcontrolbyusingnetworkapproacheshasbeenshown
tocontributetoreducingthespreadofdiseasesinvariousfarmanimalsectorssuchas
cattle,pigandfish,buthasneverbeenpreviouslyassessedinshrimp,eventhoughthe
liveshrimpmovementnetworkmakessitesveryvulnerabletodiseaseepizootics.To
implement targeted disease surveillance and control, therefore, five disease-control
algorithmswereevaluatedandtheirefficacycomparedintermsofreducingapotential
epizooticinthelarge-scalenetworkofliveshrimpmovementsofThailand(LSMN).The
resultsofthesealgorithmsshowthataneffectivestrategytocontroldiseasespreadin
theThaishrimpfarmingcanbeachievedbyremovingasmallnumberoftargetedhigh-
risk connections. Specially, two disease-control algorithms based on betweenness
centrality(thenumberofshortestpathsbetweenpossiblepairsofsitesthatrunalong
it) and subnet-crossing (connections crossing subnets within large network) are
proposedforusetoprioritisetargetsfordiseasesurveillanceandcontrolmeasuresin
Thaishrimpfarming.
5.2 Introduction
Infectiousdiseaseshavecontinuallyhitshrimpfarmingsectors.Overthelast30years,
it ismicroparasitesthathavemainlycausedsevereinfectionsinshrimp(Flegeletal.,
2008;Thitamadeeetal.,2016).Particularly,outbreaksofviralwhitespotdiseaseinthe
1990’s did not only resulted in socio-economic losses, but also contributed to the
enhancementofmanymitigationmeasures such as biosecurity (Lightner, 2005) and
diagnostictechniques(Flegeletal.,2008).Consequently,ahugefinancial investment
hasbeenmadeinthedevelopmentofdiseasesurveillanceprogrammesatnationaland
internationalscales(Bondad-Reantasoetal.,2005).Nonetheless,noapproachhasbeen
proposedtotargethigh-riskconnectionsinliveshrimpmovementnetworks.
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Recently,farmedshrimphavebecomevulnerabletoabacterialdiseasenamedacute
hepatopancreaticnecrosis(AHPND,alsoknownasEMS).Itsoutbreakcausedastrong
decreaseinshrimpexportsfromtwomajorshrimp-producingcountries(i.e.Chinaand
Thailand)ina5-yearperiodfrom2009to2013(Portley,2016).Mostconcernedly,
AHPNDcausedtheThaishrimpproductionsectortodeclinefromaround500000
tonnesin2011to200000tonnesin2014(a56%decrease;Songsanjinda,()2015).In
addition to an annual operating cost of about USD 2 Million for shrimp disease
surveillanceandcontrol(PlanningDivision,2016),in2014/2015theThaigovernment
usedanewUSD2.7million investment for themitigationofAHPND (Kongkumnerd,
2014).Duetothehighcostofdiseasesurveillanceandcontrol,regulatorsshoulddesign
disease surveillance and control programmes strategically, i.e. identifying high-risk
shrimpfarmingsitesorconnections,andthiscanbedonewithnetworkapproaches.
Manyepidemiologicalstudieshaveperformedcentralitymeasuresfortheidentification
ofhigh-risk individualswhoseremoval fromthenetworkminimisespotentialdisease
spread.Theidentificationofhigh-riskindividualsinanetworkrelatestothe80/20rule
proposedbyWoolhouseetal. (1997).Theauthorsdevelopedthisruletoexplainthe
effectsofheterogeneities insite(innetworkterminology,nodes)degreesondisease
transmission, in that generally 20%of infectious sites corresponded to80%of the
transmission.Thus,diseasesurveillanceandcontroltargetedatthe“core”20%group
canbethemosteffectivestrategy(Woolhouseetal.,2005).
Due to the rangeofnetwork structures,however,anygivenmeasure for identifying
influentialsitesorconnectionsmaynotsuitableforallnetworks(NewmanandPark,
2003). Degree centrality is proposed by many epidemiologists as a means to
demonstratethemostimportantindividualsinnetworks(Bohmetal.,2009;Christley
et al., 2005b; Zhang et al., 2010).Algorithmsbasedonbetweenness centrality have
beenshowntobethemostefficientwaytoreducepotentialepidemicsizeintermsof
component structure in the case of the network of cattle movements in France
(Rautureauetal.,2011),andthenetworkofpigmovementsinGermany(Lentzetal.,
2016).Thealgorithmbasedonbetweennesscentralityalsoreducespotentialepidemic
sizeintermsofsitereachabilityinthelivefishmovementnetworkinScotland(Green
etal.,2009).Forcomparison,othermeasuresofcentralityincludeclosenesscentrality
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(Fournie et al., 2013) and eigenvector centrality (Herrera et al., 2016). Hence, it is
importanttoaddressthreekeyquestionsinthisresearch:(1)doesanalgorithmwork
foragivensystem?(2)howdoesthisalgorithmrelatetothepropertiesofthenetwork?
and(3)whichisappropriateforThaishrimpfarming?
Thisresearch,therefore,hasaimedtofindtheoptimaldisease-controlalgorithmforthe
Thai shrimp farming system. Disease-control algorithms with and without targeted
approachesareevaluatedandcomparedusingthemovementrecordsof liveshrimp
movementsinThailand(LSMN)overthe13-monthperiodfromMarch2013toMarch
2014. The outcome of this research can form part of the process of implementing
mitigationmeasuresformanagementareasinrealtimeduringanepizooticperiod,and
for developing disease surveillance and control programmes in conventional non-
epizooticperiods,notonly for theThai shrimp farming sectorbutalsoother shrimp
producingcountries.
5.3 Materialsandmethods
5.3.1 Datasourcefortheliveshrimpmovementnetwork(LSMN)
AswithChapter4,theLSMNwithitsc.74400repeatedconnectionsintheperiodfrom
March2013andMarch2014wasthedatasourceforthisresearch.Theseofficialdata
wererecordedbyauthorisersof theThailandDepartmentofFisheries, following the
aquatic animal trade regulation of Thailand, B.E.2553 (2010). The recorded data
indicatedthefarmregistrationnumber,thesourceanddestinationoftheliveshrimp
movement,thedateofthemovement,andtheseedquantity.
The LSMN was represented by a connection-weighted adjacency matrix ℎ, an
element-wisemultiplicationofmatrix𝑎bymatrix𝑤.Theterm“site”refersto“node”in
conventional network terminology. All connections between sites were shown in a
matrix𝑎>?.Anelement𝑎𝑖𝑗tookthevalue0iftherewasnoconnectionfromsite𝑖to𝑗
and 1 otherwise,which in this case represented a potential pathway for site-to-site
diseasetransmission.Theweight𝑤𝑖𝑗denotedthefrequencyofconnectionsofsuch𝑎>?.
About 300 self-loop connections (𝑎>> = 1)were removed from the analysis because
Chapter5Disease-controlalgorithm
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theseself-loopshadnoeffectonsite-to-sitediseasetransmission(Brittonetal.,2011;
Draiefetal.,2008).
5.3.2 Disease-controlalgorithmsfortargeteddiseasesurveillanceandcontrol
Disease-control algorithmswere developed to target high-risk connections between
twosites(𝑖and𝑗)intheLSMN,whoseremovalfromthenetworkreducedthepotential
transmission of disease. The algorithms used in this researchwere developed from
thosedescribedbyGreenetal.(2012),whichcontainfourtargetedapproachesbased
on betweenness, connection weight, eigenvector centrality, and subnet-crossing to
identifytargetsforremovaland—asacontrol—anon-targetedapproach.Aneffective
algorithmshouldhavehighperformanceinreducingthesusceptibilityofanetworkto
adiseaseepizooticwithrelativelyfewremovals.
Figure5.1demonstratesthedisease-controlalgorithm.Thealgorithmbeginswiththe
whole network. Then, the connections 𝑎 from 𝑖 to 𝑗 in the network are listed in
descendingorderaccordingtooneofthefollowingcriteria.Ifconnectionshaveequal
values,theseconnectionsareselectedatrandomforeachreplicate.
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Figure 5.1 Schematic explaining disease-control algorithms with and without targeted approaches for targeted disease surveillance and control for the live shrimp movement network of Thailand (LSMN). The process is stopped when 1 000 targeted connections are removed.
Betweenness.Thebetweennesscentralityvaluewascalculatedforconnections𝑎>? in
thenetwork.Thebetweennessofaconnection𝑎>? wasdefinedbyGirvanandNewman
(2002)asthenumberofshortestpathsbetweenpossiblepairsofsitesthatrunalongit
(not compute repeated connections). The authors proposed themeasure to answer
which connections inanetworkweremost “between”otherpairsof sites.High-risk
connectionsweredetectedusingabetweennesscentralitycriterioncontained inthe
Startwiththewholenetwork
Choosethetargetaccordingtocertaincriteria: 1.Targetedapproaches 1.1Betweenness 1.2Connectionweight 1.3Eigenvector 1.4Subnet-crossing
2.Non-targetedapproach
Removetargetsbydifferentstepsizesofremovals
rangingfrom1–500removals
Evaluateimpactonthenetworkwithsitereachand
connectedcomponentsuntil1000targetsremoved
Stop
Repeated
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edge-betweennessfunctionintheigraphpackage(Brandes,2001;CsardiandNepusz,
2006).
Connectionweight.Withsimpledegreecentrality,asitewithahighdegreecentrality
score (high number of connections 𝑎>?) is considered more important for disease
transmissioninanetworkthanothersiteswithalowerdegreecentrality(Christleyet
al.,2005b;Tanakaetal.,2014).Sincethisresearchfocusedonconnections(𝑖,𝑗)witha
highweight𝑤>?,theconnection𝑎>? withhighestweightwasconsideredasatargetfor
removalfromthenetwork.
Eigenvectorcentrality.Eigenvectorcentralityhasbeenfoundtobeworthstudying
a network where some high degree sites are connected to many low degree sites
(Bonacich,2007).Theeigenvectorcentralityvalueofasitecouldbeexpressedbythe
matrix form ℎ𝑉 = 𝑉l, wherel =an eigenvalue of the network (a constant),𝑉=an
eigenvector corresponding with the eigenvalue l, and ℎ = a large, sparse, non-
symmetricmatrix (LehoucqandScott, 1996). For the LSMN, thehigh-risk siteswere
detectedusinganeigenvectorcriterionwithintheevcentfunctionintheigraphpackage
(CsardiandNepusz,2006).Ingeneral,thesitewiththehighesteigenvectorcentrality
valuecontainslotsofconnections.Inthiscase,targetedconnections𝑎>? belongingto
thissitewerechosenatrandom.
Subnet-crossing.Asubnet(subnetwork)correspondstoagroupofsitesthataremore
denselyinterconnectedthanbetweengroups.Theconceptbehindthesubnet-crossing
algorithms-basedapproachistoremoveconnections𝑎>? crossingsubnetswithinlarge
networks. Here, the fastgreedy.community subnet detection function in the igraph
packagewasused(Clausetetal.,2004;CsardiandNepusz,2006).Then,connections
thatlinktwodifferentsubnetswereindicatedwiththecommandcrossing(Clausetet
al.,2004;CsardiandNepusz,2006).Often,subnet-crossingconnectionswerehigh in
number,thusarandommethodwasusedtoselectatargetforremoval.
Non-targetedapproach(ascontrol).Inthisresearch,anon-targetedapproachwas
used to comparewith the targeted approaches. A connection𝑎>? in the LSMNwas
targetedsimplybyusingarandomisedselection.
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In order to demonstrate the effect of the removal of connections on the network
structure, after a connection removal the site reach andnetwork componentswere
calculated.
Sitereach.Thenumberofsitesreachablefromothers𝑅> representpotentialtargets
fordisease spread (Green etal., 2012). Site reachwascalculated fromanadjacency
matrix of shortest paths (𝐿>?), here computed by Dijkstra’s algorithm (Csardi and
Nepusz,2006;Dijkstra,1959).Ifthereisapathfromafocalsite𝑖toanother𝑗,thematrix
𝐿>? givesapositivevalue.Ontheotherhand,ifthereisnopathfromafocalsitetoother
the𝐿>? isdefinedasinfinity.FortheLSMN,theadjacencymatrixofshortestpaths(𝐿>?)
wascalculatedthroughtheshortest.paths functioninthe igraphpackage(Csardiand
Nepusz,2006).Then, thenumberofsites that reached𝑅> isequal to thenumberof
positivevaluesofthatfocalsite𝑖asdefinedin(5.1):
(5.1)
where[𝑋]istheIversonbracketdenoting1wherecondition𝑋istrueand0otherwise.
The maximum value of𝑅> across all sites served as an estimate of the worst-case
epidemicsize,andthemeannumberof𝑅> servesasanestimateoftypicalepidemic
size,definedin(5.2)and(5.3),respectively.Thesemeasureswereusedtoquantifythe
susceptibilityofthenetworktoanepidemic(Greenetal.,2012):
(5.2)
(5.3)
where𝑁isthetotalnumberofsitesinthenetwork.
Connectedcomponent.Wecomputedthemaximumsizeofthestronglyconnected
component(SCC),calledthegiantstronglyconnectedcomponent(GSCC),andamean
sizeofSCCforthewholenetwork.TheGSCCdeterminedthelargestnumberofsites
connectedbydirectconnectionswithinthenetwork.Theweaklyconnectedcomponent
Max reach = maxi(Ri)
Mean reach =
PRi
N
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sizes(WCC)werecalculatedinthesamewayaseitherthemaximumormeansize,in
whichthenetworkwasconsideredasundirected(Pastor-Satorrasetal.,2015).
TheSCCwasimplementedbytwoconsecutivedepth-firstsearches,andtheWCCwas
searchedviaasimplebreadth-firstalgorithm.Bothkindsofnetworkcomponentswere
computedwiththeclusters function(mode“strong”usedforSCC,andmode“weak”
usedforWCC)intheigraphpackage(CsardiandNepusz,2006).
Inthecalculationofthemeanconnectedcomponentsize,thecontraharmonicmean
wasused,insteadofthearithmeticmean.Thecontraharmonicmean𝐿p(ℂ)computes
asthearithmeticmeanofthesquaresofthevaluesdividedbythearithmeticmeanof
thevalues,definedin(5.4,Pahikkala,2010):
𝐿p ℂ =ℂ>p>
ℂ>> (5.4)
whereℂ> isthenumberofsitesincomponent𝑖.
Thisformofaveragebehavesbetterintermsofnotbeingsoaffectedbysmallisolated
groupsofsites(Moskovitz,1933).Anexampleofthearithmeticmeanof3,30and50
is (3+30+50)/3 = 27.7, whereas the contraharmonic mean 𝐿p(ℂ) of those values is
(9+900+2500)/(3+30+50)=41.
5.3.3 Usingthedisease-controlalgorithms
Eachalgorithmwasrepeatedover1000connectionremovalsusingthe𝑅Programme
Environment(Rfoundationforstatisticalcomputing,2015).Allalgorithmsweresetto
recalculatethenetworkpropertiesatthestartofeachiteration.Inaddition,duetothe
stochasticnatureofalltargetedapproachalgorithms,suchalgorithmswererepeated
1000timesandaverageresultswerepresentedintheresultssection.
Someofthesealgorithmsarecomplexandrequirerecalculationofnetworkproperties
ateachstep.Forexample,astepsizeofonewouldnaivelybeexpectedtoworkbest,
but its computation was costly, i.e. needed much computer memory or slowly
computed. Thus, as an attempt to minimise its computational complexity, in each
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approach,thehighest-rankedconnectionswereremovedfromthenetwork,withthe
numberofconnections removedat each step varied fromone to500. Forexample,
1 000 removals were performed for the step size of one connection removal at a
removal,and100removalswereperformedforthestepsizeof10connectionremovals
ataremoval.
5.3.4 Characterisingthetargetedconnections
Alltargetswerecharacterisedintermsoftheconnectionlength(km)asthestraight-line
distancebetweentwosites(Dubéetal.,2008).Basingonthegeographiccoordinatesof
theThaisub-districtsavailablefromGoogleEarth(2015),thestraight-linedistance(𝑑)
wascalculatedaccordingtothe(4.1)inChapter4.
5.4 Results
5.4.1 Thenumberofsitesreachedinthenetwork
Theresultsofusingthefivedisease-controlalgorithmswithstepsizeoneareshownin
Figure5.2.Eachalgorithmdemonstratesdifferentabilitiestoreduceboththemaximum
reachandmeanreachofsitesintheLSMN.Oncethetotalof1000connections𝑎>? was
removed, the betweenness-based algorithm performed well for both network
measures.Themaximumandmeanreachwerereducedto50%after400targetsfrom
thenetwork.Theotheralgorithms(connectionweight-,eigenvector-,subnetcrossing-,
andrandom-based)performedrelativelypoorly.Thebetweenness-basedalgorithmstill
performedwellwhenastepsizeofmorethanoneremovalwasapplied (resultsnot
shown).
Chapter5Disease-controlalgorithm
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(a)
(b)
Figure 5.2 Evaluating the disease-control algorithms against the network reachability. The betweenness algorithm performs well for both measures: (a) maximum reach and (b) mean reach. The graphs (y-axis) are plotted on a square-root (SQRT) scale to aid reading.
With regards to using different step sizes, the network snapshot at 250-removed
connectionswiththebetweennessalgorithmandtherandomalgorithmarepresented
andcomparedinFigure5.3.Thefigureshowslittleornodifferenceinbothmaximum
reachandmeanreachwhetherastepsizeofoneisusedoralargerstepsize.
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Figure 5.3 Results of different step sizes of the betweenness algorithm compared to the random algorithm at 250 removals. The estimated epizootic sizes were measured by (a) maximum reach measure and (b) mean reach. Note that the results of 20-, 100-, 200-, and 500-step sizes with (*) are 240, 200, 200, and 500 removals, as these do not divide neatly into 250.
5.4.2 Reducingconnectedcomponentsinthenetwork
Intermsofreducingconnectedcomponents,theresultsofusingthefivedisease-control
algorithmswithastepsizeofoneareshowninFigure5.4.Theeffectsofthealgorithms
onthestronglyconnectedcomponent(SCC)arenotshownhere,however,sincethis
networkmeasuregaveverysmallvaluesfortheGSCCrangingfromonetofive(witha
meanSCCofaroundone),whereastheLSMNhadagiantweaklyconnectedcomponent
(GWCC)composedofalmostallsites(13786of13801sites;meanWCC=13771).Thus,
wefocusedontheWCCinboththeGWCCandmeanWCC.
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Withthetotalof1000connectionsremoved,thesubnet-crossingalgorithmperformed
welltoreducetheGWCCandmeanWCCintheLSMN.TheGWCCslightlydecreased
from13786to13509,whilethemeanWCCdecreasedfrom13771to13490.Theother
algorithms (betweenness-, connection weight-, eigenvector-, and random-based)
performedrelativelylesswellintermsofreducingtheGWCCandthemeanWCC.The
results of other step sizes also demonstrated the good performance of the
subset-crossingalgorithms(resultsnotshown).
Interestingly,thebetweennessalgorithmhadaverysmallimpactinrespecttoreducing
boththeGWCCandmeanWCCintheLSMN.Thiscontrastswiththeearlierresultinthe
Section 5.4.1. This is presumably a result of the GWCC being generated by a large
numberof sites, and lots of connections in theGWCC showing similar betweenness
centralityscores.
Figure5.5showsthenetworksnapshotsat250connectionsremoved. Itcanbeseen
that, overall, with the subnet-crossing and random algorithms there is only a small
difference in both GWCC andmeanWCCwhether using the 1-removal step size or
biggerstepsizes.
Chapter5Disease-controlalgorithm
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(a)
(b)
Figure 5.4 Evaluating the disease-control algorithms against the weakly connected components (WCC). The subnet-crossing algorithm performed well for both measures: (a) GWCC and (b) mean WCC. The graphs (y-axis) are plotted on a square-root (SQRT) scale to aid reading. Note that the y-axis does not start at zero.
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(a)
(b)
Figure 5.5 Results of different step sizes when comparing the subnet-crossing algorithm and random algorithm at 250 removals. The estimated epizootic sizes were measured by (a) GWCC and (b) mean WCC. Note that the y-axis does not start at zero, and the results of 20-, 100-, 200-, and 500-step sizes with (*) are 240, 200, 200, and 500 removals, as these do not divide neatly into 250.
5.4.3 Thecharacteristicsoftargetedconnections
Thetargetedconnectionsfromthedisease-controlalgorithmsabovearedenotedasthe
high-risk connections fordisease spread in the LSMN. Inorder to characterise these
high-riskconnections,alltargetsfromthebetweennessandsubnet-crossingalgorithms
werecharacterisedintermsofconnectionlengths(km).Wefoundthat,whenusingthe
betweenness-basedalgorithm, thegeographicdistancesof the targeted connections
were (on average) longer, compared to the mean connection length in the whole
connections(around200km).Whenusingthesubnet-crossingalgorithm,however,the
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geographicdistancesofthetargetedconnectionswere(onaverage)shorter.Withthe
betweennessalgorithm,themeanconnectionlengthwas271km(SD=281),compared
to170km(SD=180)withthesubnetcrossing-based
Moreover, the types of source-destination pairs for the 1 000 removals, are quite
different between using the two algorithms. Tables 5.1 shows that, based on the
betweenness algorithm,most targeted connections join other siteswithin the same
typeofsite, i.e. seed-producingsites.Table5.2demonstrates thatahighnumberof
connectionsbetweendifferentsitetypes,i.e.fromseed-producingsitestoongrowing
sites,arethepriorityofthesubnet-crossingalgorithm.
Table 5.1 Source and destination site types of the top 1 000 removals from the betweenness-based algorithm shown by probabilities (in percentages) in the total number of removals, and in the whole connections (values in brackets).
Betweennessalgorithm Destination
Seed-producingsite Ongrowingsite
SourceSeed-producingsite 99.3% (2.9%) 0%
Ongrowingsite 7% (0%) 0%
Table 5.2 Source and destination site types of the top 1 000 removals from the subnet-crossing based algorithm shown by probabilities (in percentages) in the total number of removals, and in the whole connections (values in brackets).
Subnet-crossingalgorithm Destination
Seed-producingsite Ongrowingsite
SourceSeed-producingsite 7.5% (0.2%) 92.1% (2.7%)
Ongrowingsite 0.1% (0%) 0.3% (0%)
5.5 Discussion
Several approachesbasedon real networkmodelsof animalmovementshavebeen
widelyproposedforexaminingthepotentialfordiseasetransmissionandfordesigning
control strategies. A recent example is the network of livestockmovements inNew
Chapter5Disease-controlalgorithm
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Zealand,whichshowsthatremovingasmallproportionofsitesdecreasestheestimated
epizooticsize(Marquetouxetal.,2016),whereasthenetworkmodelapproachhasnow
beenimplementedinshrimpfarmingsectorinthisresearch(Chapter4“Analysisofthe
networkstructureoftheliveshrimpmovementsrelevanttoAHPNDepizootic”).
AsthemainresultofChapter4,theliveshrimpmovementnetworkofThailand(LSMN)
displayssmall-worldandscale-freeproperties.Itsstructuredemonstratesthatrandom
connectionremovaltendstobecomehighlyinefficientandcostlyasacontrolstrategy
(DezsőandBarabási,2002;Eubanketal.,2004;MayandLloyd,2001;Pastor-Satorras
andVespignani,2002b).Hence,fivedisease-controlalgorithmswereevaluatedinour
researchandtheireffectivenesscomparedontherealstructureoftheLSMN.Inorder
to limit the amount of surveillance resources, each algorithm is specified to allow a
maximumof1000removals,accountingfor3%ofallsite-to-siteconnections𝑎>? inthe
LSMN. The optimal algorithm will provide a smaller scale of estimated potential
epizootic indicated by two network measures, i.e. site reach (maximum and mean
reach) and connected components (SCC and WCC). Essentially, maximum reach is
almost like a compromise between GSCC and GWCC, and in some ways more
epizootiologicallyuseful.
In terms of site reach, the betweenness-based algorithm had high performance in
reducing the susceptibility of our studied network to a disease epizootic with few
removals (this is network structive dependent; since if the structureof thenetwork
changes,othercentralitymeasuresmaybecomemoreeffective).Theremovalofthe
3%ofconnections targetedby thebetweennesscriterionstronglycorresponds toa
decreaseinsitereachofatleast80%intheLSMN.GirvanandNewman(2002)defined
thebetweennesscentralityofaconnectionasthenumberofshortestpathsbetween
pairs of sites that passes through it. Connection removals with high betweenness
centralityscoresmeanthatthepreferentialpathwaysofmanypairsofsitesdisappear
fromthenetwork.Consequently,thesitereachintheLSMNbecomessmaller.
Considering the LSMN data,most of those high-betweenness connectionswere the
connections between seed-producing sites, which played an important role in
distributingshrimpseed(withtheirpathogens)tomanyongrowingsitesinthenetwork.
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It implies that further connections can occur shortly after the first connection,
correspondingtothelifecycleofshrimp(Quispeetal.,2016).Forexample,nurserysites
reartheseedfromnaupliusuntilpostlarvalstagewithashortperiodof20daysbefore
selling theproductionto theongrowingsites (FAO,2014).Giventhisshortperiodof
shrimpseedproduction,theoperationofthediseasesurveillanceandcontrolmeasures
alongthesehigh-betweennessconnections(e.g.testsforshrimpdiseases)needstobe
fasttoachieveanearlydetectionofdiseaseoutbreaks.
AlthoughtheconceptsofreachabilityandWCCarenotdifferent,inournetworkstudy
thetwosetsofresultsaredifferent.IntermsofWCC,targetingbasedonthesubnet-
crossingalgorithmwassuitableforreducingtheGWCCsandthemeansizeofWCCsin
theLSMN.Itcanbepresumedthatthecomponent-basedmeasuresarebeingtested
againstthemeasuresthemselves.Thus,itislikelytoberatherefficientinthatcase.As
seen,however,giventhatwith1000targetedconnectionsremovedthereisonlyasmall
decrease ofGWCCs andmeanWCCs, the component-basedmeasuremay require a
highernumberoftargetedconnectionsifitistoreduceahighestimatedepizooticsize.
Theneedfortheremovalofmoretargetedconnectionswouldresultinahighercostof
interventionintermsofdiseasesurveillanceandcontrols,andthusthesubnet-crossing
algorithmmaynotbeasuitablestrategy.
AlthoughtheGSCCwaschosenasanindicatorofmaximumpotentialepizooticsizein
thenetworkanalysisofMarquetouxetal.(2016),itisnotagoodmeasureintheLSMN.
Marquetouxetal.analysedthelivestockmovementsfromsitetositeinNewZealand,
witha largenumberof sites contained in itsGSCC (accounting for79%of the total
numberofsites;129of164).Incontrast,theGSCCintheLSMNonlyincluded0.04%of
allsites.Thestudyofepidemicsindirectednetworks,however,oftenusestheGSCCin
ordertodetermineanestimatedepidemicsizewithinthenetworks,suchaswithhuman
socialnetworks(Eubanketal.,2004),andanimalcontactnetworks(Kissetal.,2006;
Rautureauetal.,2011).OnemajoradvantageoftheGSCCmeasurewasproposedby
Volkovaetal.(2010)andKaoetal.(2007).TheydescribedthattheGSCCapproached
thelowerboundofthefinalepidemicsizeintheabsenceofcontrolstrategies,aswith
thelatedevelopmentofpathogendiagnosisinthecaseofAHPND.Thus,ifthestructure
of LSMN possesses a large strongly connected component, the GSCC may become
Chapter5Disease-controlalgorithm
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compelling in designing targeted disease surveillance and controls for Thai shrimp
farming.
Although, the use of a larger step size had little or no effect on the reduction of
estimated epidemic sizes, whether measured by site reach and WCC, it gave an
importantadvantage,allowingfastcomputationsforalarge-complexnetworklikethe
LSMN. A few fast algorithms for computing network centrality have already been
developed (Bader et al., 2007; Brandes, 2001;Madduri et al., 2009; Shi and Zhang,
2011). It appears, however, that most algorithms in the literature use one-step
removals.ThestudyofGreenetal.(2012),forexample,conductedone-stepremovalto
evaluate the effects of targeted removal for the directed network of fish farms. By
removingmorethanoneconnection,acloselyrelatedexampleispresentedinNatale
et al. (2009). They specified the number of targets before simulating the epidemic
networkmodelssuchasthe1%ofsiteswiththehighestcentralityscores.Aswellas
allowingfastercomputations,theuseofbiggerstepsizes(morethanoneremoval)may
helptodesigndisease-controlalgorithms.
Insummary,inanattempttoreducethespreadofdiseasesintheLSMNbyremovinga
limited number of connections between sites, the targeted strategies performed
relativelywellcomparedtoanon-targetedapproach.Thisresearchwasdoneonthe
single network layer of live shrimpmovement. Different resultsmight arise, if local
contact or water-borne contact were included. This complex mode of disease
transmissionleadsustouseepizooticnetworkmodelsintoAHPND,asaddressedinthe
followingchapter.
Chapter5Disease-controlalgorithm
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Chapter 6 - Epizootic disease modelling in farmed shrimp using compartmental epizootic network-based simulations
N.Saleetid;D.M.Green;F.J.Murray
Preface
The dynamics of an acute hepatopancreatic necrosis disease (AHPND) epizootic are
modelledinordertoacquireabetterunderstandingofitsepizoologyandtoexamine
the effect of two potential pathways for AHPND epizootics (long-distance and local
spread)withintheThaishrimpfarmingindustry.Theliveshrimpmovementdata(LSMN)
usedinChapters4and5alsoservesasamajordatasourceinthischapter,aswellas
thesurveydatacollectedinChapter3providesinformationonincubationandfallow
periodsforAHPND,andachanceofinfectionvialocalspread.TheRcodesforthemodel
simulationwerewritten by the author,with input fromD.M.Green. The chapter is
designedintheformatofpublicationstohelpusprepareapotentialjournalpaper,and
thus,natureofAHPNDisdescribedagaininthisintroductionsection.
Impactstatement
Althoughtestingforinfectiousdiseasesbeforemovementsoffarmedshrimpisawell-
established principle in Thailand, in some cases diseasesmay not be detected. This
makes it more difficult to design disease controls, and provides a motivation for
modellingAHPNDepizooticdynamics,especiallysinceoutbreaksofAHPNDhavecaused
massiveshrimpproductionlossesinThailand.Thepurposeofthismodelistoevaluate
theeffectoflong-distanceandlocalspreadrelativetoAHPNDepizooticsinThailandand
tosuggestmitigationmeasures.Poorbiosecuritypractices(e.g.waterdischargeandlow
efficiencyofdiseasescreeningofliveshrimpmovements)arethereforeassumedinthe
model. The results of the model could emphasise the importance of biosecurity in
stoppingthespreadofdiseaseacrossshrimpfarmingsites.
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Chapter 6 - Epizootic disease modelling in farmed shrimp using compartmental epizootic network-based simulations
6.1 Abstract
Sincethefirstoutbreakofacutehepatopancreaticnecrosisdisease(AHPND)inThailand
in2011/2012,therehasbeenlittleinthewayofepizootiologicalstudyofitsepizootic
dynamics.Here,therefore,an𝑆𝐸𝐼𝑅𝑆compartmental,individual-basedepizooticmodel
is used to explore potential AHPND spread within the real live shrimp movement
networkofThailand(LSMN)ina13-monthperiod.Inaddition,themodelexaminesthe
effectoftwopotentialpathwaysforthespreadofAHPND:long-distancetransmission
vialiveshrimpmovementsandlocaltransmission(e.g.sharedwaterbodiesforfarming
and sites located in close proximity). The results reveal that AHPND epizootics in
Thailand aremore likely to occur during hot and rainy seasons (between April and
August). The inter-province movements are potentially a major source of AHPND
epizootics.Withlowerratesoflong-distancetransmissioninthemodel(𝛽6789 <1),the
numberofinfectedsiteswassmaller.Inthemodel,localtransmissionalone(𝛽6789was
0 and𝛽67;<6 was 0.002) was responsible for very small epizootic sizes. Themodels
thereforesuggest that theAHPNDepizooticdynamics inThai shrimpfarmingcanbe
minimisedbyenhancingbiosecuritymeasuresinrespecttoliveshrimpmovements,and
interventions to reduce local spreadshouldbeconsideredaspartofcurrentdisease
surveillanceandcontrolmeasures.
6.2 Introduction
Acutehepatopancreaticnecrosisdisease(AHPND)isanewepizooticbacterialdisease
infarmedshrimp,firstidentifiedbyLightneretal.(2012).Itcausesseveremortalityof
shrimpofupto100%ininfectedsites.Diseasedshrimphaveobviousvisiblesignsof
AHPND,i.e.emptygutandstomach,andpaleoratrophiedhepatopancreas.Mostsites
infectedbyAHPNDhavebeenfoundtohaveincreasedshrimpmortalitywithin10–35
dayspoststockingofpostlarvae(FAO,2013;NACA,2014).
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183
Thescaleofbacterialepizooticsinshrimpfarmingisnotlessthanofthatviralepizootic.
AHPNDhascausedhugeglobaleconomiclossesduetothefactthatitsepizooticsoccur
throughout the major producing countries, i.e. China, Vietnam, Thailand, Malaysia,
MexicoandthePhilippines(Dabuetal.,2015;EduardoandMohan,2012;Nunanetal.,
2014). Since AHPND first appeared in Southern China in 2009, it still persists in the
shrimp farming sector (NACA, 2017; Zorriehzahra and Banaederakhshan, 2015).
Importantly,theemergenceofAHPNDisencouragingshrimpstakeholdersthroughout
theworldtoundergoadramatictransitioninfarmingpractices(EduardoandMohan,
2012;FAO,2013).TheeraoftheAHPNDepizooticisthereforewellrecognisedbymany
shrimpstakeholdersasbeingeitheracrisisforshrimpaquacultureorthebeginningof
much-neededimprovementsinshrimpfarming
MovementsofinfectedshrimpareamajorrouteforAHPNDtransmission(OIE,2013a;
Tran etal., 2013b).Alternatively, the specific strainsofVibrioparahaemolyticus, the
causative agent of AHPND, can spread via the live feeds commonly used in
seed-producingsitessuchaspolychaetewormsandbivalves(Ushijima,2014).Thecase–
controlstudyofBoonyawiwatetal.(2016)alsofoundthatthesourceofshrimpseed
relatedtotheoccurrenceofAHPNDatsite level.Shrimpfarmershavealsoobserved
thatmassmortalitiesofshrimphaveoccurredinAHPND-infectedsiteshaveoccurred
eveninnewlypreparedponds(FAO,2013).Accordingtothisevidence,manyfarmers
suspect that seed-producing sites generate long-distance transmissionofAHPND, as
describedaboveinChapter3.
Diseaseoutbreaksviaphysicalproximityofsitesandtheirhydrologicalconnectivityare
classified as local transmission with non-contact network spread. This follows from
commonactivitiessuchassharingofwatersupplies (GalappaththiandBerkes,2015;
Smith,1999)anddischargingwaterfromculturedponds(Muniesaetal.,2015;Patilet
al.,2002;Vandergeestetal.,1999).Importantly,pondwaterandsoilserveasagood
mediumforthecausativeagentofAHPNDtogrow(Chonsinetal.,2016;Kongkumnerd,
2014;Tranetal.,2013a).Hence,thereislikelytobemorethanonepathwayinfluencing
thedynamicsofAHPNDepizooticdynamics,makingitmoredifficultforregulatorsto
designdiseasecontrols.
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184
Inmanycases,efficientdiseasecontrolscanbeachievedbyusingepidemicandnetwork
modelling approaches. Epidemic network models attempt to capture the epidemic
dynamicsinapopulationinwhichrealconnectionsexistbetweenindividualsandallow
potentialdiseasetransmission(KeelingandEames,2005).Inhumandiseases,the𝑆𝐼𝑅
(susceptible-infectious-recovered)epidemicmodelhasbeenrecentlymodelledforthe
spreadofEbolainLiberia(Rizzoetal.,2016).Epidemicmodelsforthespreadoffoot-
and-mouthdiseaseintherealnetworksof livestockmovements(DurandandMahul,
2000; Rvachev and Longini, 1985) have also provided many new insights and
epizootiologicaltoolsthatcanbeappliedtootherfarmedanimaldiseases,suchascarp
(Tayloretal.,2011)andsalmon(Werkmanetal.,2011).Importantly,thesemodelshave
resultedinthedevelopmentofeffectivediseasepreventionandcontrolmethods.An
AHPNDmodelthereforerepresentsaverypowerfultooltoassistinthedevelopmentof
suitablemeasurestopreventlargeAHPNDoutbreaks.
An improvedunderstanding of the dynamics of theAHPNDepizootic among sites is
requiredinordertosupportdiseasepreventionandcontrol.Inaddition,althoughwe
knowmuch about the infection at site level, this does notmean thatwe know the
epizootic at country scale. Thus, we have developed an 𝑆𝐸𝐼𝑅𝑆 compartmental,
individual-based epizootic model in this research to: (1) examine AHPND epizootic
dynamicsinThaishrimpfarmingsites,and(2)analysetheeffectoflong-distanceand
local transmission, including the effect of established biosecurity measures on live
shrimpmovements.Thisisthefirstepizooticmodellingofbacterialdiseasebasedon
therealnetworkofliveshrimpmovements.
6.3 Materialsandmethod
6.3.1 Theliveshrimpmovementnetwork(LSMN)
A traceability system for farmed shrimp developed by the Thailand Department of
Fisheriesprovidedaccesstotherecordsofdailyliveshrimpmovementsamong13801
sites𝑁[7[<6 (seed-producingsites=804,andongrowingsites=12997)inthe396-day
periodfrom1March2013to31March2014.Overthisperiodtherehadbeenonsetsof
AHPNDandothershrimpdiseasessuchaswhitespotdisease,yellowheaddisease,and
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185
taura syndrome (NACA, 2017). The records contained a source site of shrimp, a
destinationsiteofshrimp,amovementdate,andaquantityofshrimpseed,usefulin
determining the country-wide dynamics of infectious diseases. Multiple records
representingabatchmovedwithinadaywerecombinedasonerecord.Intotal,our
modelwasbasedoncirca74000records.
Inthisresearch,anadjacencymatrixℎ>?[ofsize𝑁[7[<6×𝑁[7[<6×396dayswasusedto
representtheLSMNmathematicallysuchthatthetimingwasanactualconnectiondate
fromsite𝑖tosite𝑗onday𝑡.Theℎ>?[wastheelement-wisemultiplicationofamatrix
𝑎>?[bymatrix𝑤>?[onday𝑡.Element𝑎𝑖𝑗𝑡tookthevalue1iftherewasaconnectionfrom
asite𝑖toasite𝑗implyingapossiblepathfordiseasetransmissionfromsite𝑖to𝑗,and
𝑎𝑖𝑗𝑡tookthevalue0otherwise.About300self-loopconnections(𝑎>>[=1)wereremoved
from the analysis because these self-loops did not contribute to further spread of
diseases(Brittonetal.,2011;Draiefetal.,2008).
UnlikeinChapters4and5,wheretheweight𝑤𝑖𝑗𝑡denotedthefrequencyofconnections,
eachelementaddressedinthispartwasthenumberofshrimpseedintheconnections
betweenthesamepairsofsites(inunitsofabillionshrimp)underanassumptionthat
aconnectionwithalargernumberofinfectedshrimpwouldoffermoreriskofpathogen
transmissionthanconnectionswithfewerinfectedshrimp.
6.3.2 Localcontactsbetweenshrimpfarmingsites
Eitherphysicalproximityofsitesortheirhydrologicalconnectivitywasassumedtopose
ariskoflocaltransmissionforAHPND.Thesub-districtareawasusedtodeterminethe
locationofsites(moredetailoftheThailocalgovernmentalunitswasgivenbyNagaiet
al., 2008).Anundirectedblockmatrix𝑏>? of size𝑁[7[<6×𝑁[7[<6 determined the local
connectionsbetweensitesinthesamesub-district(notincludingself-loops).Themodel
assumedthatthelocalnon-networkspreadwithinsub-districtswasofamixingrandom
type:eachsitecouldinfectallmembers,exceptingforsub-districtswithonlyonesite.
Asitelocatedinasub-districtwithahighernumberofinfectedandexposedsiteswould
presumablyhavemorechanceofbecominginfectedthanothers.
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186
Thefrequencyofthenumberofsitemembersforthe748sub-districtsrelevanttothe
LSMNisillustratedinFigure6.1.75%ofthesub-districtshadequaltoorlessthan21
sitemembers(i.e.the75thpercentilefor748sub-districts=21).
Figure 6.1 Frequency of number of site members per sub-district. Most sub-districts had ≤ 21 site members (the 75th percentile for 748 sub-districts = 21).
6.3.3 An𝑺𝑬𝑰𝑹𝑺compartmental,individual-basedepizooticmodelforacutehepatopancreaticnecrosisdisease(AHPND)
An𝑆𝐸𝐼𝑅𝑆compartmental,individual-basedepizooticmodelwasconstructedtoexplore
AHPND transmission among the Thai shrimp farming sites. Two constructs were
involved in themodel: (1) all shrimp farming sites were taken from the LSMN, i.e.
seed-producingsitesandongrowingsites,and(2)thedirectednetworkofliveshrimp
movements(Section6.3.1)andtheundirectednetworkoflocalspread(Section6.3.2)
withtheformerencompassingtwoscenarios:scenarioA(bothintra-andinter-province
movements)andscenarioB(intra-provincemovementsalone).
Allsiteswereclassifiedintooneoffourstates:susceptible(𝑆),exposed(𝐸),infected(𝐼)
andremoved(𝑅);theirstatesvariedovertime.The‘𝑆’statewasnotinfected,butwas
stockedandcouldbeinfected.The‘𝐸’statewasinfectedbutwithouteithervisiblesigns
ofAHPNDorunusualshrimpmortality.Sitesat‘𝐸’state,however,couldposeariskto
othersites.Thenextstate, ‘𝐼’,wasinfectedandhadincreasedshrimpmortalitywith
clinicalsignsofAHPND.Similartotheexposedsites,the‘𝐼’couldposearisktoothers.
149
120
177 172
130
0
50
100
150
200
1 2-3 4-10 11-30 31-100
Freq
uency
Numberofsitememberspersub-district
Chapter6𝑆𝐸𝐼𝑅𝑆network-basedmodel
187
Thefinalstate‘𝑅’wasfallow,meaningthatthesitewasnotstockedandinusefora
shrimpproduction.Accordingly,the‘𝑅’sitecouldnotbeinfected.
Tosimulateanepizootic,themodelbuildingconsistedofthefollowingthreesteps:
6.3.3.1 Initialisationofmodelsimulation
Epizooticswere started in eachmonth to explore the seasonal effects. During each
simulation,theinitialinfectedsites(seeds)withafinitefractionofseedsat0.02were
selectedat randomfromthe804seed-producingsites in theLSMN(=16seeds).All
othersiteswereassumedtobesusceptible.Hence,avector𝐸>[representedthestate
of site 𝑖 at initialisation 𝑡 = 𝑡r: 0 for susceptible, infected and removed, and 1 for
exposed.
ModelparameterswereestimatedbasedonthedetailsofthespreadofAHPNDand
Thaishrimpfarmingactivitiesreportedintheliterature,andthesurveyeddatathatwas
collectedintheChapter3.Theprobabilitiesofinfectionviaaliveshrimpmovement(𝑓)
andlocalspread(𝑔)aredescribedinsection6.3.3.2.
Incubation,infectious,andfallowperiodsforeachsiteweredifferent.Tocharacterise
twooftheseperiods(incubationandfallow)threedistributionswerefittedtothedata
collectedduring theepizootiological survey in theChapter3 (Weibull, lognormal, and
gamma)(Figure6.2).Thesecandidatedistributionshaverecentlybeenemployedinthe
researchofTojinbaraetal.(2016).Thelognormaldistributionwasfoundtobethebest
fit to the observed incubation periods of a microparasitic disease, followed by the
gamma distribution, whereas the Weibull did not fit well (Bénet et al., 2013).
Nevertheless, the Weibull distribution is usually found to be the best fit to the
emergenceofbacterialdiseases(Chen,2007;Henryonetal.,2005;Laietal.,2016).
Chapter6𝑆𝐸𝐼𝑅𝑆network-basedmodel
188
(a) (b)
Figure 6.2 Density plots of fitted distributions of the data for incubation periods (a) and fallow periods (b) in days.
ThebestfittingdistributionprovidedalowerAkaike'sInformationCriterion(AIC)value
asinLeclercetal.(2014)andBénetetal.(2013).Table6.1showstheresultsoffitting
ofthethreedistributionstothedatainthisstudybymaximumlikelihood.Considering
the AIC values, the Weibull distribution was the best fit to the observed AHPND
incubation periods (AIC = 339), and the gamma distributionwas the best fit to the
observed fallow periods (AIC = 576). In the modelling, therefore, the sites were
randomly given different periods, distributed according to these appropriate
distributions.Thesestepswerecomputedusingthe𝑅ProgrammeEnvironmentwith
the fitdistrplus package (Delignette-Muller and Dutang, 2015; R foundation for
statisticalcomputing,2015).
Table 6.1 Akaike's Information Criterion (AIC) values of fitted distributions. The Weibull distribution gives the smallest AIC value for incubation period data while the gamma distribution gives the smallest AIC value for fallow period data.
Observeddata Weibull Lognormal Gamma
Incubationperiod 339 351 346
Fallowperiod 578 582 576
When the sites were infectious, over half of the farmers (65 %, 61of94) in the
epizootiological survey of the Chapter3 indicated their intention to stop stocking
suddenlyafterinfection(herewecalledthisfarmerbehaviour“typeI”).Thisbehavioural
Incubationperiod(days) Fallowperiod(days)
Density
Chapter6𝑆𝐸𝐼𝑅𝑆network-basedmodel
189
intentionimpliedashort-terminfectionatthesites.Incontrast,theremainingfarmers
triedtotreattheinfectedstockbystoppingfeeding,loadingwithprobiotics,andwater
exchangewithinthepond,resultingineithermoderate(28%,26of94:typeII)orlong-
terminfection(7%,7of94:typeIII).Thus,themodelproceededusingthefollowing
twosteps:(1)themodelassignedoneofthesethreetypesofbehaviourtoeachsite
(bothseed-producingandongrowingsites)whilekeepingallproportionsconstant(65
%of𝑁[7[<6 fortypeI,28%of𝑁[7[<6 fortypeII,and7%of𝑁[7[<6 fortypeIII),and(2)
theinfectionperiodwasgeneratedrandomlyforeachsiteunderthreeassumptions:a
1–7 day short-term infection for a group of farmers behaviour type I, an 8–30 day
moderate-terminfectionforagroupoffarmersbehaviourtypeII,anda31–120day
long-terminfectionforagroupoffarmersbehaviourtype III.
Themodelfixedtheexpectedperiodsforincubation,infectiousandfallowforeachsite
beforerunningasimulation.Thismeantthatifasitewasinfectedtwiceitbehavedthe
sameeachtime.
6.3.3.2Simulationapproach
Theepizooticsweremodelledusinga1-daytimestep.Assumingthataconnectionwith
a larger number of shrimp would carry more risk of pathogen transmission than
connectionswith fewer shrimp,we used the number of shrimpmoved,𝑐 (6.1) as a
weightfortheinfectiousnessofshrimp,𝜔(6.2):
(6.1)
(6.2)
where𝜇=1denotingtheinfectiousnessofshrimpactproportionaltotheirnumber.
Ateach1-daytimestep,avector𝑓representedaprobabilityofdestinationsites(𝑗)
becomingexposedviatheconnectionsof liveshrimpfromexposed𝐸and infected𝐼
sourcesites(𝑖),asin(6.3):
cjt =X
i
(Eit + Iit)hijt
!jt = cµjt
Chapter6𝑆𝐸𝐼𝑅𝑆network-basedmodel
190
⌘
(6.3)
where𝛽6789=1impliesveryhighriskofinfectionviatheconnectionsofinfectedshrimp
since,inthepresentcircumstance,any𝜔 > 0causesinfection.
The 𝑓 included the stochastic nature of the epizootic process (sites successfully or
unsuccessful treated for AHPND). A vector 𝑄 of size 𝑁[7[<6 relied on a
𝐵𝑒𝑟𝑛𝑜𝑢𝑙𝑙𝑖distribution:0fornon-exposedand1forexposedwithaprobability=𝑓,asin
(6.4).
(6.4)
Additionally,aneffectoflocaltransmissionwasdependentonthenumberofinfections
(bothexposed𝐸andinfected𝐼sites)withincloseproximity,𝑧(6.5).Thecloseproximity
ofsiteswasbasedonthemodelledsub-districtareas.Themathshereworkedinparallel
totheabove.
(6.5)
The𝑧wasusedasaweightfortheinfectiousnessofsite(),asin(6.6):
(6.6)
where𝜖=1denotestheinfectiousnessofsites,actingproportionaltothenumberof
theirneighbours.
Avector𝑔representedtheprobabilityofsitesbecomingexposedvialocalspread,asin
(6.7):
(6.7)
where𝛽67;<6 =0.002perdayasthechanceofinfectionduetolocalnon-networkspread.
ThiswasestimatedfromthenumberofAHPNDcases(2012–2013)inThailand(vialocal
fjt
= 1� (1� �long
)!jt
Qjt ⇠ Bernoulli(fjt)
zjt =X
i
(Eit + Iit)bij
Chapter6𝑆𝐸𝐼𝑅𝑆network-basedmodel
191
Ei + Si + Ii +Ri = 1
andlong-distancespread),basesontheresultsfromtheChapter3.Additionally,𝛽67;<6
=0.1perdayweremodelledtoevaluateanincreaseoflocalspreadeffect.
Althoughasusceptiblesitewasatriskofacquiringdiseasecausedbyinfectionvialocal
spread, the sitemightnotbe infectedasa resultofgood farmingmanagementand
control.Totakeaccountofthisinthemodel,𝑔wasincludedtocapturethestochastic
natureoftheepizooticprocesses.Anewvector𝑌ofsize𝑁[7[<6 wasgeneratedbythe
sameprocedureasusedforthevector𝑄,asin(6.8),but1denotingexposedreliedon
aprobability=𝑔.
(6.8)
Thenewstatevectorafterexposuretoinfectionwas:
(6.9)
where
Then,themodelranoverthetimeperiodoverwhichinfectionoccurred.Asdescribed
in the initialisation section, each site had a given length of time spent in its state
following each period for incubation, infection, and fallow. Hence, when a sitewas
exposed to infection, it was in the ‘𝐸’ state for the incubation period. After the
incubation period ended, the site entered the ‘𝐼’ state (𝐸 → 𝐼;𝐼> = 1, 𝐸> = 0). The
furtherchangesof‘𝐼’and‘𝑅’weredependentontheinfectiousperiod(for𝐼 → 𝑅;𝑅> =
1, 𝐼> = 0)andthefallowperiod(for𝑅 → 𝑆;𝑆> = 1, 𝑅> = 0),respectively.
6.3.3.3Finalresults
Atthefinalstageofthemodelsimulation,thenumberofinfectedsites‘𝐼’wascounted
andplottedeachdayoverthe13-monthnetworkdataperiod.Overall,themodelling
processcanbesummedupasshowninFigure6.3.
Ei,t+1 = Eit + (1� Eit)(1� (1�Qjt)(1� Yjt))
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192
Figure 6.3 Design and implementation of an algorithm for an 𝑺𝑬𝑰𝑹𝑺 compartmental, individual-based epizootic model for shrimp disease in Thailand
Theepizooticsimulationmodelwascarriedout inthe𝑅ProgrammeEnvironment(R
foundation for statistical computing, 2015). The model was run 80 times for each
epizooticstarted.Intotal,1040resultswereobtained(13months×80simulations)per
parametersetstudied;thesewereusedtocomputethemeannumberofinfectedsites,
representinganestimatedepizooticsizeforAHPND.
Geographic distributions of AHPND prevalence at provincial level of Thailand were
shownintheresult.Thenumberofinfectedsites(𝐼)duringagivenperiodwasobtained
by themodelling of𝛽6789=1 and𝛽67;<6 =0.002. The prevalence of AHPND at each
provincewascalculatedastheproportions(%)ofsitesinfectedwithAHPND(6.10).
Proportions =Numberofinfectedsitesinaprovince
Totalnumberofinfectedsites ×100 (6.10)
The AHPND-infected site proportions were plotted in map form to represent the
geographic distributions of AHPND prevalence in Thailand. A map of Thailand as a
Initialisation
- Setupseedandmodelparameters,thensetinitialstatevectorof model
Mainloop
- Introducenetworksofdailyliveshrimpmovementsandlocalcontacts
- Calculatenewstatevectors,thencopythemallbacktothestatevectorsfornexttime step
Finalresults
- Countnumberofinfectedsiteseachday- Plotagraph
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193
shapefilewasdownloadedfromhttp://www.diva-gis.org/gdata.Thismapwasmodified
using the rgdal package in the 𝑅 Programme Environment (Bivand et al., 2015; R
foundation for statistical computing, 2015), aswell as the tmap package (Tennekes,
2017).
To evaluate the accuracy of the disease prevalence prediction generated using the
𝑆𝐸𝐼𝑅𝑆model,themodelestimateswerecomparedagainsttherealpatternofAHPND
epizooticsinThailandatprovinciallevelreportedinJuly2013byFAO(2013).Thereal
AHPNDepizooticsinThairegionsandprovincesareshowninTable6.2.
Table 6.2 Real pattern of AHPND epizootics within shrimp farming sites of Thailand reported in July 2013 (FAO, 2013)
Region Diseasepresence Province
West NoAHPNDcase
Northeastern NoAHPNDcase
Central AHPNDcase Nakhonpathom
East AHPNDcase Chachoengsao,Chanthaburi,Rayong,andTrat
South
AHPNDcase Chumphon,Krabi,Nakhonsithammarat,Phuket,Songkhla,andSuratthani
Moreover,thereceiveroperatingcharacteristic(ROC)curvewasusedtoevaluatethe
model performance in predicting a binary outcome (Park et al., 2004). Here we
presentedthreetestsontheROCanalysis:
(1)Diseaseprevalenceofthe𝑆𝐸𝐼𝑅𝑆modelagainstdiseaseprevalenceinthefieldas
reportedbyFAO(2013),
(2)Diseaseprevalenceofasimplernullmodelwiththenumberofsitesagainstdisease
prevalenceinthefieldasreportedbyFAO(2013),and
(3)Diseaseprevalenceofasimplernullmodelwiththenumberofconnections(inter-
provincemovements‘connectionsin’andintra-provincemovements)againstdisease
prevalenceinfieldasreportedbyFAO(2013).
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194
Weuseddifferentcut-offvaluesofa“predictor”tomakethepredictorbinary inthe
ROCanalysis.Thepredictorvariedwiththepredictivediseaseprevalenceobtainedfrom
eachtestmodel.WiththeareaundertheROCcurve(AUC),themodelwasclassifiedto
beeitheraninformativemodel(AUC>0.5;thepredictiveresultofmodelisbetterthan
random)oranuninformativemodel (AUC=0.5; thepredictiveresultofmodel isnot
differentfromrandom)(AlonzoandPepe,2002;KumarandIndrayan,2011).TheROC
analysis was done in the 𝑅 Programme Environment (R foundation for statistical
computing,2015).
6.4 Results
6.4.1 SeasonalityofAHPNDepizooticdynamics
Themeannumberofsites infectedbyAHPNDdueto long-distanceand local spread
(default parameters𝛽6789was1 and𝛽67;<6 was 0.002) is shown in Figure 6.4. These
resultsareobtainedfromthesimulationsofthe𝑆𝐸𝐼𝑅𝑆models,andtheybasedonthe
realnetworkofliveshrimpmovementsofThailand(LSMN)overthe13-monthperiod
fromMarch2013toMarch2014.
The figure illustrates that inter-province movements are the major source of large
epizooticsaccordingtoourmodels.Wecanseethatthereisadifferenceinthenumber
ofinfectedsitesbetweentwoscenarios:A(bothintra-andinter-provincemovements),
andB(intra-provincemovementsalone).
WithscenarioA,wherethelargerepizooticsaregeneratedfrombothintra-andinter-
provincemovements,theresultsofthe𝑆𝐸𝐼𝑅𝑆modelsrevealthatAHPNDepizooticsin
Thailandaremorelikelytooccurduringthehotandrainyseasons(betweenApriland
August), and are less likely to happen throughout the rest of the year (mainly cool
season).With scenario B,meanwhile, where the epizootics are generated from the
intra-provincemovements alone, the𝑆𝐸𝐼𝑅𝑆 models gave amuch lower number of
infected sites and a shorter period for the risk ofAHPND (betweenApril and June),
comparedwiththescenarioA.
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195
Themodelresultsforthetwoscenariossuggestthatcontrolstrategiesofrestrictions
on inter-provincemovements of live shrimp are obviously required tominimise the
spreadofAHPND.
Figure 6.4 Mean number of infected sites per seed for one-month epizootics. Infection occurs via long-distance and local spread (default parameters 𝜷𝒍𝒐𝒏𝒈 was 1 and 𝜷𝒍𝒐𝒄𝒂𝒍 was 0.002). The averaged values of 1 040 epizootics are shown, where 16 seeds were selected at random in each month (seed fraction was 0.02). The results were generated from two data sets: both intra- and inter-province movements (scenario A; solid line), and intra-province movements alone (scenario B; dash line).
6.4.2 Effectoflong-distanceandlocaltransmissiononAHPNDepizooticdynamics
Themodelalsoprovidedthechancetosimulatetrialbiosecuritymeasures,i.e.testing
for diseases in farmed shrimp before movements, and disease surveillance
programmes.ThemodelswithscenarioAuseddifferenttrialvaluesof𝛽6789=0,0.5and
1.Parameter𝛽67;<6 was0.002,andtheseedfractionwas0.02.
TheresultsareshowninFigure6.5.Ifthemovementsreliedonbiosecuritymeasures
(𝛽6789waslower),thenumberofinfectedsitesdecreasedmarkedlyandtheremaining
numberofinfectedsizeswasverysmall.Importantly,inthemodels,localspreadalone
couldsupporttheAHPNDepizooticsintheThaishrimpfarming(𝛽6789was0and𝛽67;<6
was0.002). This indicated that even if the Thai shrimp farming sector fully applied
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biosecurityonliveshrimpmovements,asmallnumberofseeds(16seeds)couldcause
anAHPNDepizooticvialocalspread.
Figure 6.5 Expected outcomes of the application of biosecurity measures on live shrimp movements in Thailand. With 𝜷𝒍𝒐𝒏𝒈 varied from 0 to 1, the lower 𝜷𝒍𝒐𝒏𝒈 gave smaller epizootics. The model set 𝜷𝒍𝒐𝒄𝒂𝒍 at 0.002, and the seed fraction at 0.02.
Toevaluatetheeffectofincreasedlocalspread,theepizooticswerecomparedwithtwo
valuesof𝛽67;<6 (0.002and0.01perday)inscenarioA.Thedefaultparameter𝛽6789was
settobeone.Figure6.6showsthatthegreaterlocalspreadresultsinahighernumber
ofinfectedsites.Interventionstoreducelocalspreadshouldthereforebeconsidered
aspartofcurrentdiseasesurveillanceandcontrolmeasures.
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Figure 6.6 Effects of larger local spread in Thai shrimp farming sectors. The higher local spread (𝜷𝒍𝒐𝒄𝒂𝒍 was 0.01) caused a higher number of infected sites, compared with setting 𝜷𝒍𝒐𝒄𝒂𝒍 at 0.002. The model set 𝜷𝒍𝒐𝒏𝒈 at 1, and the seed fraction at 0.02.
6.4.3 GeographicdistributionsofAHPNDprevalenceatprovinciallevelinThailand
Accordingtothemodel,thegeographicaldistributionsofAHPNDprevalenceonday153
(July 2013, 31) is presented in Figure 6.7a. Themodel shows that the southern and
eastern provinces of Thailandwere at greater risk of AHPND than other areas. The
resultsweresimilartotherealpatternofAHPNDepizooticsinThailandreportedinJuly
2013 as shown in Figure 6.7b (FAO, 2013 see Table 6.1). In respect to thewestern
provinces, however, the model clearly overestimated the number of AHPND, as in
realitynoneoccurredinthewestduringthestudiedperiod.
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(a) (b)
Figure 6.7 Geographic distributions of AHPND-infected provinces in Thailand. (a) The prevalence of AHPND between March 1st 2013 and July 31st 2013, are modelled using the 𝑺𝑬𝑰𝑹𝑺 models. The epizootic models started with 16 seeds in each month. Model parameters of 𝜷𝒍𝒐𝒏𝒈 and 𝜷𝒍𝒐𝒄𝒂𝒍 were 1 and 0.002, respectively. (b) The real AHPND presence in each province of Thailand as reported in July, 2013 (FAO, 2013).
6.4.4 Predictiveperformanceofthe𝑺𝑬𝑰𝑹𝑺models
Tofurthertestthemodelvalidity,theROCapproachwasusedtoshowtheAUCvalues
for threetestmodels: the𝑆𝐸𝐼𝑅𝑆model, thesimplernullmodelwith thenumberof
sites,andthesimplernullmodelwiththenumberofconnections.
Figure6.8showsthatalltestmodelshavegoodpredictiveperformancecomparedwith
anuninformativemodel(AUCof0.5).TheAUCvaluesofthe𝑆𝐸𝐼𝑅𝑆model,sitenumber
model,andconnectionnumbermodelwere0.88,0.87,and0.91,respectively.Although
theAUCofthe𝑆𝐸𝐼𝑅𝑆modelisclosetothatotheroftheothermodels,the𝑆𝐸𝐼𝑅𝑆model
isasystematicapproachandamoreflexibletoolforpredictingsitesatrisktoAHPND.
Iftheconnectionsbetweenshrimpfarmingsitehavetheconsistencyintermofnetwork
structure,the𝑆𝐸𝐼𝑅𝑆modeldoesnotrequirethereal-timeinformationformodelling.
Highprevalence
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Figure 6.8 ROC curves of three test models identifying the presence of AHPND in Thai shrimp farming sites. The ROC curves are plots of true positives, i.e. where the model gave positives for provinces identified as having AHPND (plotted on the y axis) versus false positives, i.e. where the model gave positives for provinces identified as having no AHPND (plotted on the x axis). The disease presence in the field reported by FAO (2013) was used to compare with the disease presence in the 𝑺𝑬𝑰𝑹𝑺 model, and for disease presence in the two simpler null models: prediction by number of sites, and number of connections.
6.5 Discussion
Thisstudyhasmodelledtheepizootiologicalpatternofanewbacterialdiseasenamed
acutehepatopancreaticnecrosisdisease(AHPND,alsoknownasEMS) inThaishrimp
farming, and has examined the effect of long-distance and local transmission on its
epizooticdynamics.Theepizooticestimationreliedonan𝑆𝐸𝐼𝑅𝑆(susceptible-exposed-
infected-remove-susceptible)model. Inaddition, theepizooticmodel simulationwas
controlledby the realnetworkof live shrimpmovementsofThailand (LSMN)overa
13-month period from March 2013 to March 2014. As a consequence, the model
systematically characterised the importance of two studied pathways for AHPND
transmission,therebyinformingthedesignofdiseasesurveillanceandcontrolforwhole
shrimpfarmingsitesoratacountry-widescale.
WhenliveshrimpmovefromalocationofhighAHPNDincidence,theycarrytheriskof
infection.Thus,patternsofliveshrimpmovementscanreflecttheseasonalityofAHPND
Falsepositiverate
True
positivera
te
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Diseaseprevalenceestimatedbysitenumber
Diseaseprevalenceestimatedbyconnectionnumber
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epizootics in Thailand. Our model demonstrated that AHPND was likely to occur
betweenAprilandAugust(duringthehotandrainyseasons inThailand).Duringthis
period,thenumberofsiteconnectionstotallyincreasedbyabout50%,asshowninthe
Chapter4“Analysisofthenetworkstructureoftheliveshrimpmovementsrelevantto
AHPNDepizootic”,withapproximatelyhalfofthesehavingoriginallymovedfromthe
south. To implement successful control strategies, seed-producing sites in southern
areas should bemonitored closely for the AHPND pathogen, such as increasing the
samplingfrequencypriortotheoccurrenceofregularoutbreaks.
Intermsoflong-distancetransmissioneffects,inter-provincemovementsplayamajor
role inAHPNDepizootics,while intra-provincemovementsalonealsogeneratesmall
epizootic sizes.Althoughan importantbiosecuritypractice, i.e. testing forAHPND in
farmedshrimpbeforemovingtheshrimpfromsitetositehasbeenimplementedinthe
Thaishrimpfarmingsector(Uddin,2008;YamprayoonandSukhumparnich,2010),one
concern iswhether currentdiseasecontrolmeasuresarebasedon thesensitivityof
diagnostictechniquesanddelaysindiagnosis(i.e.thetimebetweensymptomonsetand
establishment of diagnosis) among infected sites (Ahmed et al., 2016; Lightner and
Redman, 1998; Liu et al., 2016; Saulnier et al., 2000a; Sithigorngul et al., 2007).
Additionally,previousworkhasindicatedthatvibriosiscanbefoundinhealthyshrimp,
which makes the infectious shrimp difficult to detect and treat at the initial stage
(Aguirre-Guzmánetal.,2004;Goarantetal.,2006;Gomez-Giletal.,1998;Vincentand
Lotz,2005).Fromthisevidence, it ispossible that the infectedshrimparestillbeing
accidentallymovedbetweensites.
Moreover, local transmission alone can cause AHPND epizootics according to our
modelling, although these epizootics are not large. Nevertheless, in this case if the
diseaseoccursinareaswithahighnumberofsites,orinasitewithhighconnections,
many infected sites may be observed across large areas. This local spread can be
generatedbycommonactivitieswithinThaishrimpfarmingsuchassharingofwater
bodies,aswellasbythecloseproximityofshrimpfarmingsites(Boonyawiwatetal.,
2016;Hazarikaetal.,2000).Waterdischargefrominfectedsitestocanals,riversorthe
sea,alsocauseslocalspreadincreases,ifthosesitesdonotconductappropriatewater
disinfection. Additionally, vibrios are often found growing in aquatic environments
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(Kongkumnerd,2014),makingshrimpfarmingsitesvulnerabletoreceivingpathogens
vialocalspread.Thesepathwaysdrivinglocalspreadneedtobeeliminated.
Thediseasecontrolstrategiessuggestedbythisproposedmodelarebiosecurityonlive
shrimpmovementsandmonitoringofdiseasesinthenaturalenvironment.Themodel
isusefulfortestingcontrolstrategiesbychangingmodelparameters.First,applyinga
lowerchanceofinfectionviatheconnectionsofinfectedshrimp(𝛽6789)inthemodelling
resulted in smaller sizesofpotentialAHPNDepizootics.This illustrated thatapplying
biosecuritymeasurestoshrimpmovementscouldeffectivelycontrolAHPNDepizootic
dynamics.Anothertestwaschangingthechanceofinfectionduetolocalnon-network
spread (𝛽67;<6).The increased likelihoodof local spread led to largerepizootics.This
emphasised the importance of monitoring diseases in the natural environment of
shrimp farmsandalso theneed forwaterdisinfection for shrimp rearing. Theseare
commonpracticesofbiosecurity,but theymightbeneglected in recentThai shrimp
farming.
Theremovedsites(𝑅)inour𝑆𝐸𝐼𝑅𝑆modelmightbeinfected.Nevertheless,iftheyare
dryingout,forexample,andnotconnectedtowatersourcesorcarryingshrimp(called
sitefallowing),theyprobablydonotposeononwardriskofinfectionbecauseofalack
of host-pathogen-environment interactions (Rockett, 1999). In real practice, Thai
shrimpfarmersconductthefallowingprocessinvaryingperiodsfrom3to60days(data
obtained from the epizootiological survey in Chapter 3), although the Thailand
DepartmentofFisheriesrecommendsthatallsitesshouldbefallowedforbetweenone
andfourweeksafterharvestingshrimp(NationalBureauofAgriculturalCommodityand
FoodStandards,2014;ThailandDoF,2010).Thus,anappropriateperiodof fallowing
requiresfurthermodelling.
Amodelwillneveraddressexactlythereal-worldcomplexity.Itjustmeansthatasimple
solutiontoacomplexproblemmaywork.Forexample,inourmodels,theoccurrence
ofAHPNDwasoverestimatedinsomeareas.Nevertheless,themodelprovidedmore
accuratepredictionsofAHPNDoccurrencethanrandomguessmodels.Forcomparing
the performance of the three different test models, the value of the ROC curves
obtainedfromthe𝑆𝐸𝐼𝑅𝑆modelwasclosetothatofthetwosimplernullmodels(as
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describedintheresultssection).The𝑆𝐸𝐼𝑅𝑆epizooticmodelwasheavilydrivenbythe
connectiondata(liveshrimpmovements),thustheobtainedresultwasnotunexpected.
Alsoofconnectionsverycloselywithsitecountthatwasnotunexpectedeithersince
the site counts are known to relate to low or high numbers of connections.
Nevertheless,notethatdatahereaggregatesatahighlevel(province),andaggregation
atasitelevelmaythereforeshowdifferentresults.
Oneofthelimitationsofthisresearchwasthatthemodelwasnotgreatlyconcerned
withtypesofshrimpfarmingsites,i.e.large,mediumandsmallscales.Differenttypes
ofsiteprovidevariousfarmingmanagementpractices(Erleretal.,2007;Lebeletal.,
2010).Themodeldid,however,considerthenumberofshrimp.Large-scalesitesthat
commonlyrelatetostockingahighnumberofshrimpshouldhavehighvulnerability
toinfectioncomparedwithsmallandmediumscales.Takingaccountofthiswouldbe
expectedtomakethemodelmoreaccurate.
Tosummarise,theAHPNDepizooticdynamicsweremodelledintheThaishrimpfarming
industryusingan𝑆𝐸𝐼𝑅𝑆compartmental,individual-basedepizooticmodel.Theresults
suggestedthatboth long-distanceand local transmission influencedthedynamicsof
AHPNDepizooticinThaishrimpfarming.Oftheeffortstocontroltheseepizooticsatthe
countryscale,biosecurityof liveshrimpmovementsand interventionstoreducethe
local spread (e.g.monitoringofpathogens innatural environments) are required. In
futuremodellingofAHPNDepizooticdynamics, control strategies in respect to local
spreadwillbeevaluated.
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Chapter7Generaldiscussion
209
Chapter 7 - General discussion
7.1 Summary
Thisfinalchaptersummarisesthemainconclusionsoftheresearch.Inthelastpartof
thischapter,wesuggestsomedirectionsforfurtherwork.
The main objective of this research was to apply epizootiological tools to study of
AHPND and other infectious disease spread in the Thai shrimp farming industry.
Chapter 3 (the first research chapter), we evaluated the risk factors for AHPND
occurrenceatsitelevelusingacross-sectionalstudydesign.TheAHPND-infectedcases
wereidentifiedfromadecisiontreethatwasdevelopedbasingongrosssignsofAHPND
andnon-laboratoryconfirmation.Theresultsfromunivariateanalysisandconditional
logisticregressionindicatedthatnopondharrowingincreasedtheriskofAHPNDand
the use of earthen ponds for shrimp rearing decreased the risk of the disease
occurrence.
Chapter4utilisedthedataoftheliveshrimpmovementnetwork(LSMN)toexamine
thespreadofinfectiousdiseasesinshrimpfarmingsites.Theresultsemphasisedthat
theLSMNhadahighnumberofinter-provincemovements,whichincreasedthechance
ofdiseaseepizootics acrossprovinceand regionbordersof Thailand.Moreover, the
LSMNrepresentedascale-freenetworkwithsmall-worldproperties.Thissuggeststhat
regulatorsanddecisionmakersneedtoposemoreattentionineffortstodesigndisease
surveillanceandcontrolstrategies.
Consequently, in Chapter 5 disease-control algorithms were developed using four
targetedapproachesandanon-targetingapproach.Theseaimedtofindoptimalcontrol
strategies to reduce the potential epizootic size in the LSMN. The results from the
studied network demonstrate that targeted strategies, i.e. biosecurity measures in
respecttotargetedliveshrimpmovements,supportthesuccessofdiseasesurveillance
andcontrolinThaishrimpfarming,andimprovethecost-effectivenessofsurveillance
resources.
Chapter7Generaldiscussion
210
Chapter6modelledthespreadofAHPNDfromsitetosite,basedontwotransmission
routesforthedisease(long-distanceandlocaltransmission).An𝑆𝐸𝐼𝑅𝑆compartmental,
individual-basedepizooticmodelshowedthatAHPNDwaslikelytooccurduringthehot
andrainyseasonsofThailand(betweenAprilandAugust).Duringthisoutbreakperiod,
themovementsweremainlysourcedfromtheareasinthesouth.Althoughthenumber
ofintra-provincemovementswassmall(23%oftotalconnections),theyalsoaffected
theestimatedepizooticsize.Fordiseasepreventionandcontrol,themodelsindicated
thatlowerlong-distancetransmissionrates(𝛽6789 <1,and𝛽67;<6 was0.002)strongly
reduced thenumberof infectedsites. Local spreadalone,however, couldstill cause
epizootics(𝛽6789was0,and𝛽67;<6 was0.002).GiventhesetwomajorroutesforAHPND
transmission, two measures are suggested in this research. First is an increase in
biosecurityonliveshrimpmovements(effectivetestingofdiseasesinfarmedshrimp
beforemovements,andtargeteddiseasesurveillanceandcontrolofdiseasespreadin
the live shrimp movement network). The second is interventions to reduce local
transmissionsuchasmonitoringofdiseasesinnaturalenvironments.
7.2 Generaldiscussion
7.2.1 Diseasecaseconfirmation
Chapter3developedanAHPNDdecisiontreefortheidentificationofAHPND-infected
cases,basedonthediseaseatthepointbeinganunknownetiology.Thiseffectivetool
wasabletonarrowthegapperiodfordiseasepreventionandcontrols.Thetoolalso
couldalsohelpthedecisionoffarmerstodealwiththeinfectionatsitelevel,providing
evidencetosupporttheearlystoppingofanypathwayfortransmittingpathogens,such
as stoppingwaterdischarge tonatural sources.Theoutcomeshould lead to smaller
sizedepidemics,asproposedinFergusonetal.(2001).Amatterofgreatconcernisthat
waitingforlaboratoryresultscausedthedelayedoutbreakreporting,andconsequently
the late implementation of mitigation measures, as happened with a waterborne
outbreakofGiardiasis(agastrointestinaldisease) intheNorwegianpopulationin2004
(Nygård et al., 2006). Hence, until there is a rapid diagnosticmethod for infectious
diseases, the decision tree will remain useful to the study epidemiology of these
diseases.
Chapter7Generaldiscussion
211
The AHPND decision tree can reflect on current disease surveillance and control
programmes in Thailand. Shrimp samples obtained from both active and passive
surveillancesareconfirmedbythePCRtesting(adiagnosistoolforAHPND).Thistestis
quitecostly(approximately25USDpersample)andtime-consuming(aroundtwodays)
(CoastalAquaticAnimalHealthResearchInstitute,2016b).Thus,thedecisiontreecan
beusedasaprimaryscreen todetectcasesofAHPND.Theuseof thedecision tree
techniqueasascreeningtoolfordiseaseshasalreadybeenshowntobeeffectiveinthe
epidemiologicalinvestigationofrheumatoidarthritisinalargedatabaseofaroundtwo
millionpatients(Zhouetal.,2016).Foranotherpurpose,thedecisiontreegivesdecision
makers a tool to choose optimised control measures for foot-and-mouth disease
transmittedbetweenlivestockanimals(Tomassenetal.,2002).
7.2.2 Shrimpfarmingdatausedforepizoology
7.2.2.1Datarecordedonshrimpfarmingmanagementandpractices
DuringtheepizootiologicalsurveyinChapter3,akeysuccessforthedatacollectionwas
thatmany interviewees hadwritten their daily farming practices in their own diary
booksorelectronic files.Therecordingfollowstherequirementofgoodaquaculture
practice (GAP) standards for Thai shrimp farming (National Bureau of Agricultural
CommodityandFoodStandards,2014),withmostshrimpfarmingsitesinThailandnow
being certified by the GAP standard (Fisheries Commodity Standard System and
TraceabilityDivision,2017).Thus,theintervieweescouldprovideaccuratedatatous,
leading to robustepizootiological studies.The recordingof farmingactivities ismost
helpfulinepizoologyifsuchrecordingiscollectedforpurposesofdiseasesurveillance
andcontrols(Birkheadetal.,2015;Wendtetal.,2015).
7.2.2.2Liveshrimpmovementnetworkdata
Itshouldbeemphasisedthattheliveshrimpmovementnetworkdataprovidesrobust
epizootiological information.Modellingthesedataasanetworkprovidedapowerful
toolfortheunderstandingofdiseasespreadbetweensites(Chapter4).Additionally,its
modellingaidedthedesignofdiseasesurveillanceandcontrolstrategiesthatmightbe
applied given limitations of surveillance resources (Chapter 5). Nonetheless, the
Chapter7Generaldiscussion
212
networkmaychangeitsstructureoverthelongtermbecausepatternsofconnections
change(Greenetal.,2012;Stoddardetal.,2013).Implementationsofcontrolstrategies
such as vaccination (McReynolds et al., 2014), and case isolation and quarantine
(Lipsitchetal.,2003;Pandeyetal.,2014)alsoinfluencethestructureofnetworks.Thus,
furtherresearchisneededtodeterminetheconsistencyofthisnetworkstructure,in
whichsuchhistoricaldataoftheliveshrimpmovementnetworkprovideabenchmark.
ThenetworkdatathatweobtainedfromtheThailandDepartmentofFisheriescanbe
consideredtobehighlyaccuratefortworeasons.First,thedatawerebasedonofficial
reports;second,suchinformationwerekeptincomputeriseddatabase.Nonetheless,
thereweresomeminorfailuresthatshouldbementioned.Errorswerefoundinrespect
tositeaddresseswithinthevillagedetails. Ifwereliedonthelocationofsitesatthe
lowest level of the village, therefore, these errorswould affect the reliability of our
results.Hence,computerdataareonlyasgoodasthatwhichareentered.
7.2.3 Modellingdiseaseepizooticdynamics
Thenetworkmodelling inChapters4and5 considers thepotential routeofdisease
transmission(liveshrimpmovements)butinrealitymostshrimpdiseasescantransmit
fromsitetositeviaotherpathways,suchashydrologicalconnectivityandthephysical
proximity of sites (see Chapter 1). In addition, the non-random mixing and
heterogeneityofconnectionsaffectdiseasetransmission.Thus,wemodelledthelive
shrimpmovementsasaweightednetwork,andtooktheeffectof localnon-network
spread and the number of shrimp into account while modelling AHPND epizootic
dynamics(Chapter6).
Thecapacityoffarmerstomanagetheirsitesafterinfectionalsoplaysanimportantrole
indiseaseepizootics.Wuetal.(2001)proposedthatthemortalityofwhitespotdisease-
challengedshrimpthatwerestockedathighdensitybecamecriticalifmoribundshrimp
were not immediately removed as to minimise the cannibalism and waterborne
transmission.Theevidenceindicatesthatmodelsfordiseasespreadsbetweenanimal
farmsitesshouldconsiderthefarmers’behaviourindiseasepreventionandcontrolas
anexampleinChapter6.
Chapter7Generaldiscussion
213
7.3 Futurework
7.3.1 Controlstrategiesforlocalnon-networkspread
To control disease spread via live shrimpmovements, Thailand applied a process of
testing farmed shrimp for diseases before their movements (National Bureau of
Agricultural Commodity and Food Standards, 2015). Additionally, Thai regulators
routinelymonitorfordiseasesinfarmedshrimpthroughactiveandpassivesurveillance
programmes (Coastal Aquatic Animal Health Research Institute, 2016a). Here, a
targeted disease surveillance approach was developed in Chapter 5 using network
modellingapproacheswiththeaimofimprovingthesesurveillanceprogrammes.While
the effect of the local non-network spread on the AHPND epizootic dynamics was
demonstratedinChapter6,thebenefitofthetargetedstrategiesforlocalnon-network
spreadwasnotclearinpractice.
Commonly,Vibriospp.arefoundtobefreelivinginaquaticenvironments(Thompson
etal.,2004).Theirlifecyclesoftenrelatetolocalnon-networkspreadofdiseases(i.e.
hydrologicaltransmission)forfarmedshrimp(Apanakapanetal.,2016;Heenatigalaand
Fernando,2016;Naganathanetal.,2014;Puietal.,2014).Importantly,theabundance
ofVibriospp.innaturalwatersourcescanbepromotedbycohabitationwithprotozoans
(Laskowski-ArceandOrth,2008).Thus,targetingofnetworkspreadmaynotsufficient.
For this case, hydrodynamicmodelsmayneed tobe a part of thedesignof control
strategies.Parallelexamplesforthisincludesalmonlice,whichareabundantonboth
wildandfarmedfish(Torrissenetal.,2013),andforwhichmanyhydrodynamicmodels
havealreadybeendeveloped(GillibrandandWillis,2007;MurrayandGillibrand,2006).
7.3.2 Coinfectionepizooticmodels
DuringAHPNDoutbreaks,farmedshrimpinAsiafaceanotherinfectiousdiseasenamed
hepatopancreaticmicrosporidiosis (HPM)causedbyamicrosporidianEnterocytozoon
hepatopenaei (NACA, 2015; Thitamadee et al., 2016). HPM does not lead to mass
mortalities, but it is associatedwith slow growth in affected stocks (Salachan et al.,
2017).Nevertheless,recently,Arangurenetal.(2017)foundthatHPM-infectedshrimp
haveahighriskofAHPNDinfection.Theevidenceshowsthatshrimpareinfectedby
Chapter7Generaldiscussion
214
bothHPMandAHPND,leadingtomorecomplexdynamicsofAHPNDepizooticsthan
ourmodelsinChapter6.Additionalparameters,suchasaprobabilityofHMPinfection,
thereforeneedtobeaddedtothemodelsdevelopedinthisresearch.
7.3.3 Geographicalinformationsystems(GIS)forshrimpfarmingsites
Withapproachesofcomputerisationandgeographical informationsystems(GIS),the
description of a disease epidemic in terms of real spatial results can be undertaken
(Cromley,2003;Tamietal.,2016).ThisisbecauseGIScanworkonareas,whilemany
networkmodelssuchasourstreatsitesessentiallyaspointsources.Forhumandisease
models,GIS has been used to examine transmission of tuberculosis (Moonan et al.,
2004), chikungunya fever (Rodriguez-Morales et al., 2016), and dengue disease
(Palaniyandietal.,2014),forexample.GIShasalsorecentlybeenusedinthemodelling
of livestock diseases such as bovine tuberculosis (Ribeiro‐Lima et al., 2016). For
aquatic animals, GIS is typically applied to identify suitable areas for aquatic animal
farming(Giapetal.,2005;Salametal.,2003;Salametal.,2005),neverthelessGIShas
beenlesswidelyusedinshrimpandfishepizoology(Bayotetal.,2008).
ForThaishrimpfarming,theGISapproachcouldallowusmoreaccuracyinsitelocation
to determine a change in the distance of movements caused by any rewiring.
Additionally, the geographic distribution of AHPND, and the spread of other shrimp
diseasesinThailand,couldbedrawnatsitelevelinsteadofprovincelevelasshownin
Chapter6.
7.4 Conclusions
- Theuseofepidemiologicalandepizootiologicaltoolsdemonstratedfourmain
epizootiologicalfindingsforAHPNDspreadinThaishrimpfarming.
- A cross-sectional study revealed a significant association between AHPND
transmission at site level and farming management practices, i.e. types of
ongrowing ponds and harrowing. No pond harrowing increased the risk of
AHPNDonset,whileearthenpondsdecreasedtheriskofAHPNDoccurrencein
ongrowingsites.
Chapter7Generaldiscussion
215
- Moreover, case confirmation with the AHPND decision tree allowed rapid
investigationsoftheriskfactorsfortheAHPNDtransmission,giventhatatthe
timeofdatacollection,AHPNDwasadiseasewithanunknownetiology.This
case-identification AHPND decision tree can be adapted when there is
recurrenceofAHPND,orthegrosssignsofdiseasepathologycanbechanged
whenthereisincidenceofothernewdiseases.
- Inordertopreventandcontrolinfectiousdiseaseoutbreaksatthecountryscale,
networkmodellingisappliedhereforthefirsttimetotheshrimpfarmingsector.
ThisillustratedthepropertiesoftheliveshrimpmovementnetworkinThailand
(LSMN)inaidingandhinderingthetransmissionofdiseases.Mostimportantly,
our research into network modelling emphasised that the random disease
surveillanceandcontrolcouldnoteffectivelyminimisediseasespreadsitetosite
via long-distance transmission, compared with the optimal targeted disease
surveillanceandcontrol.
- Accordingtoour𝑆𝐸𝐼𝑅𝑆compartmental,individual-basedepizooticmodel,the
inter-provincemovementswerethemainsourceofAHPNDspreadintheThai
shrimpfarming.The𝑆𝐸𝐼𝑅𝑆modellingstronglydemonstratedthattheincrease
ofbiosecuritywithintheliveshrimpmovementnetworkcouldleadtosmaller
potentialAHPNDepizootics.Additionally,itsuggestedthattherewasaneedfor
diseasesurveillanceandcontrolinnaturalsourcesorotherpathwaysrelativeto
localnon-networkspread.
- Ournetworkepizooticmodelmayapplytootherdiseasesandindustries.The
modelcanbeappliedtootherdiseasesbyusingdifferentparametervalues,or
addingotherpathwaysfordiseasetransmission.Themodelcanbeappliedto
other industries by including other types of sites (e.g. fish, mussel and crab
farming, or processing plants) and their connections if the data is available.
Importantly, the model is useful to evaluate current regulations or desired
strategies to prevent and control disease outbreaks such as biosecurity and
surveillancestrategies.
Chapter7Generaldiscussion
216
The epidemiological and epizootiological tools used in this research provide unique
contributionstothestudyofshrimpepizoology.Theresultsgivemoreunderstandingof
site-to-sitetransmissionofAHPNDandotherinfectiousdiseasesinfarmedshrimp.Thai
regulatorsanddecisionmakerscanusetheminimprovementofdiseasesurveillance
andcontrol.
Chapter7Generaldiscussion
217
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AppendixAShrimpdiseasepictures
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Appendix A: Shrimp disease pictures
Picture cards showing gross signs of AHPND were shown alongside those showing clinical signs of other high-prevalence shrimp diseases while collecting data for AHPND cases and non-cases.
AppendixAShrimpdiseasepictures
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Appendix A: Shrimp disease pictures
Picture cards showing gross signs of AHPND were shown alongside those showing clinical signs of other high-prevalence shrimp diseases while collecting data for AHPND cases and non-cases.
AppendixBQuestionsfortelephonesurvey
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Appendix B: Questions used in brief telephone survey
(Notethattheanswersbaseonshrimpfarmingfromyear2011tocurrent)
Questions:
1. Does your farm face any disease problem? If ‘yes’ please tellme the name of
disease?
2. IfthefarmisinfectedbyAHPND,WhendoyouseetheAHPND-typelossesforthe
firsttime?Whatarethesignsoftheshrimpthatareclinicallyill(Thaispeakerscall
กุง้ป่วย/Kûng-P�wy/)includingtheageofAHPND-diseasedshrimp?
3. Howmany ponds affected with AHPND?What are themortality rates in each
affectedpond?
4. Pleasegivemethedetailsaboutgeneralmanagementpractices
- Featuresofongrowingponds
- Ponddryingduration
- Pondharrowingbeforestocking
5. Whatmeasuresdoyoutaketomitigatediseasetransmissionorrecurrence?
AppendixCNationalprovincialcentres
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Appendix C: National provincial centres and abbreviation
Provincecode Nationalprovincialcentre Abbreviation
11 Samutprakan SPK18 Chainat CNT73 Nakhonpathom NPT72 Suphanburi SPB61 Uthaithani UTI74 Samutsakhon SKN13 Pathumthani PTE14 Phranakhonsiayutthaya AYA26 Nakhonnayok NYK75 Samutsongkhram SKM10 Bangkok BKK60 Nakhonsawan NSN12 Nonthaburi NBI23 Trat TRT24 Chachoengsao CCO21 Rayong RYG20 Chonburi CBI22 Chanthaburi CTI25 Prachinburi PRI30 Nakhonratchasima NMA77 Prachuapkhirikhan PKN70 Ratchaburi RBR71 Kanchanaburi KRI76 Phetchaburi PBI90 Songkhla SKA94 Pattani PTN80 Nakhonsithammarat NRT84 Suratthani SNI86 Chumphon CPN82 Phangnga PNA81 Krabi KBI83 Phuket PKT96 Narathiwat NWT91 Satun STN85 Ranong RNG92 Trang TRG93 Phatthalung PLG