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Introduction Methods and Results Conclusions 1 / 18
Dynamic Drivers of Disease in Africa
Integrating our understandings of zoonoses, ecosystems and wellbeing
A unified framework for the infectiondynamics of zoonotic spillover and spread
Gianni Lo Iacono1,21 Department Veterinary Medicine University of Cambridge, UK2 Departmen of Environmental Change, Public Health England, UK
April 4, 2016
Introduction Methods and Results Conclusions 2 / 18
Acknowledgments
Andrew A. Cunningham 2, Elisabeth Fichet-Calvet 3, Robert F. Garry 4,Donald S. Grant 5, Melissa Leach 6, Lina M. Moses 4, Gordon Nichols 7 JohnS. Schieffelin 8, Jeffrey G. Shaffer 9, Collen Webb 10, James L. N. Wood 1
1 Department of Veterinary Medicine, Disease Dynamics Unit, University of Cambridge, Cambridge, UnitedKingdom.2 Institute of Zoology, Zoological Society of London. United Kingdom3 Bernhard-Nocht Institute of Tropical Medicine. Hamburg, Germany4 Department of Microbiology and Immunology, Tulane University, New Orleans, Louisiana, USA5 Lassa Fever Program, Kenema Government Hospital, Kenema, Sierra Leone6 Institute of Development Studies, University of Sussex. Brighton, United Kingdom7 Public Health England, United Kingdom8 Sections of Infectious Disease, Departments of Pediatrics and Internal Medicine, School of Medicine, TulaneUniversity, New Orleans, LA, USA9 Department of Biostatistics and Bioinformatics, Tulane School of Public Health and Tropical Medicine, NewOrleans, LA, USA10 Department of Biology, Colorado State University, Fort Collins, USA
Introduction Methods and Results Conclusions 3 / 18
Key Questions
Setting up traps in the mining areain Sierra Leone
Blood sampling in bats in Ghana Participatory mapping in SierraLeone
If we know the abundance of,
the infection prevalence in, andexposure to
the reservoir,
can we estimate the likelihood of thenext spillover event?
Introduction Methods and Results Conclusions 3 / 18
Key Questions
Setting up traps in the mining areain Sierra Leone
Blood sampling in bats in Ghana
Participatory mapping in SierraLeone
If we know the abundance of, the infection prevalence in,
andexposure to
the reservoir,
can we estimate the likelihood of thenext spillover event?
Introduction Methods and Results Conclusions 3 / 18
Key Questions
Setting up traps in the mining areain Sierra Leone
Blood sampling in bats in Ghana Participatory mapping in SierraLeone
If we know the abundance of, the infection prevalence in, andexposure to the reservoir,
can we estimate the likelihood of thenext spillover event?
Introduction Methods and Results Conclusions 3 / 18
Key Questions
Setting up traps in the mining areain Sierra Leone
Blood sampling in bats in Ghana Participatory mapping in SierraLeone
If we know the abundance of, the infection prevalence in, andexposure to the reservoir, can we estimate the likelihood of thenext spillover event?
Introduction Methods and Results Conclusions 4 / 18
Key Questions
From: Lloyd-Smith, J et al. (2009). Epidemic dynamics at thehuman-animal interface. Science, 326(5958).
A need for unification
How to compare different stages?How to disentangle the contribution ofhuman-to-human transmission from zoonoticspillover?
Introduction Methods and Results Conclusions 4 / 18
Key Questions
From: Lloyd-Smith, J et al. (2009). Epidemic dynamics at thehuman-animal interface. Science, 326(5958).
A need for unificationHow to compare different stages?
How to disentangle the contribution ofhuman-to-human transmission from zoonoticspillover?
Introduction Methods and Results Conclusions 4 / 18
Key Questions
From: Lloyd-Smith, J et al. (2009). Epidemic dynamics at thehuman-animal interface. Science, 326(5958).
A need for unificationHow to compare different stages?How to disentangle the contribution ofhuman-to-human transmission from zoonoticspillover?
Introduction Methods and Results Conclusions 5 / 18
Dynamcs of Lassa Fever
Human-‐to-‐Human Transmission through close contacts, probably via body fluids. Previous es8mates suggest ~ 20% of cases can be a?ributed to human-‐to-‐human transmission
Nosocomial Transmission. e.g. exchange of infected needles
Rodent-‐to-‐Rodent Transmission. Unclear reasons for maintenance. Transmission pa?erns are further confounded due to seasonality in Mastomys natalensis. abundance and in infec8on prevalence. These factors are also affected by the habitat (rodents living near houses vs rodents living in the proximity of villages)
Mastomys natalensis
Repor6ng Bias. Many cases are not reported despite improvement in community outreach and surveillance ac8vi8es. Infrastructure quality (roads are oJen flooded during the rainy season), economic and social factors (people have limited economic resources in the rainy season) might introduce seasonal bias in repor8ng.
Rodent-‐to-‐Human Transmission. Transmission through domes8c/agricultural exposure.
Introduction Methods and Results Conclusions 6 / 18
Modelling the risk of spillover events
Introduction Methods and Results Conclusions 7 / 18
Modelling the risk of spillover events
0.0
0.1
0.2
0.3
0 10 20 30Occurrence
Den
sity
From knowedge of mean andvariance of abundance, prevalenceetc. → Infer the risk of spillover inhumans
Introduction Methods and Results Conclusions 8 / 18
Patterns in the cumulative number of cases
Straight line for pure zoonosisComparison with Agent Based Model
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0
1000
2000
3000
0 100 200 300 400 500 600 700Day
Cum
ulat
ive
Num
ber
of Z
oono
tic I
nfec
tions
Introduction Methods and Results Conclusions 9 / 18
Depletion of susceptibles
Introduction Methods and Results Conclusions 10 / 18
Patterns in the cumulative number of cases
Concave (downward) line when susceptibles are depletedComparison with Agent Based Model
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Introduction Methods and Results Conclusions 11 / 18
Inclusion of human-to-human transmission
Introduction Methods and Results Conclusions 12 / 18
Patterns in the cumulative number of cases
Convex (upward) line when human-to-human transmissionComparison with Agent Based Model
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an In
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Introduction Methods and Results Conclusions 14 / 18
Contribution of human-to-human transmission
Comparison with Agent Based Model
a"Parameter 1
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0
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0 25 50 75 100Day
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num
ber o
f Zoo
notic
and
Hum
an−t
o−H
uman
Infe
ctio
ns
Introduction Methods and Results Conclusions 15 / 18
Comparison with real data from Sierra Leone
Constant zoonotic exposure
Red Line − : Kenema Gov. Hospital dataBlue Lines − : 5 individual stochastic model realizationsBlack Line −: Average model predictionsGrey dots · · · : 100 stochastic model realizations
Introduction Methods and Results Conclusions 15 / 18
Comparison with real data from Sierra Leone
Constant zoonotic exposurePiecewise, linearly variable zoonotic
exposure
Red Line − : Kenema Gov. Hospital dataBlue Lines − : 5 individual stochastic model realizationsBlack Line −: Average model predictionsGrey dots · · · : 100 stochastic model realizations
Introduction Methods and Results Conclusions 15 / 18
Comparison with real data from Sierra Leone
Constant zoonotic exposurePiecewise, linearly variable zoonotic
exposure
Red Line − : Kenema Gov. Hospital dataBlue Lines − : 5 individual stochastic model realizationsBlack Line −: Average model predictionsGrey dots · · · : 100 stochastic model realizations
A case of un-identifiabilityDifferent assumptions are equally compatible with the empiricaldata
Such un-identifiabilityis is expected to be removed as soon asmore accurate data on exposure rates and rodent infectionprevalence become available.Only a general knowledge of the time-dependency of thesequantities
Introduction Methods and Results Conclusions 15 / 18
Comparison with real data from Sierra Leone
Constant zoonotic exposurePiecewise, linearly variable zoonotic
exposure
Red Line − : Kenema Gov. Hospital dataBlue Lines − : 5 individual stochastic model realizationsBlack Line −: Average model predictionsGrey dots · · · : 100 stochastic model realizations
A case of un-identifiabilityDifferent assumptions are equally compatible with the empiricaldataSuch un-identifiabilityis is expected to be removed as soon asmore accurate data on exposure rates and rodent infectionprevalence become available.
Only a general knowledge of the time-dependency of thesequantities
Introduction Methods and Results Conclusions 15 / 18
Comparison with real data from Sierra Leone
Constant zoonotic exposurePiecewise, linearly variable zoonotic
exposure
Red Line − : Kenema Gov. Hospital dataBlue Lines − : 5 individual stochastic model realizationsBlack Line −: Average model predictionsGrey dots · · · : 100 stochastic model realizations
A case of un-identifiabilityDifferent assumptions are equally compatible with the empiricaldataSuch un-identifiabilityis is expected to be removed as soon asmore accurate data on exposure rates and rodent infectionprevalence become available.Only a general knowledge of the time-dependency of thesequantities
Introduction Methods and Results Conclusions 16 / 18
Conclusions and Future Work
A unified framework for spillover and stuttering chain
Signature for identify human-to-human transmission, andprocedure to quantify it....but it is a signature that can be forged!Future Work: Remove Un-identifiabilityFuture Work: Impact of super-spreaders
Introduction Methods and Results Conclusions 16 / 18
Conclusions and Future Work
A unified framework for spillover and stuttering chainSignature for identify human-to-human transmission, andprocedure to quantify it..
..but it is a signature that can be forged!Future Work: Remove Un-identifiabilityFuture Work: Impact of super-spreaders
Introduction Methods and Results Conclusions 16 / 18
Conclusions and Future Work
A unified framework for spillover and stuttering chainSignature for identify human-to-human transmission, andprocedure to quantify it....but it is a signature that can be forged!
Future Work: Remove Un-identifiabilityFuture Work: Impact of super-spreaders
Introduction Methods and Results Conclusions 16 / 18
Conclusions and Future Work
A unified framework for spillover and stuttering chainSignature for identify human-to-human transmission, andprocedure to quantify it....but it is a signature that can be forged!Future Work: Remove Un-identifiability
Future Work: Impact of super-spreaders
Introduction Methods and Results Conclusions 16 / 18
Conclusions and Future Work
A unified framework for spillover and stuttering chainSignature for identify human-to-human transmission, andprocedure to quantify it....but it is a signature that can be forged!Future Work: Remove Un-identifiabilityFuture Work: Impact of super-spreaders
Introduction Methods and Results Conclusions 17 / 18
Acknowledgments
This work for the Dynamic Drivers of Disease in Africa Consortium was funded withsupport from the Ecosystem Services for Poverty Alleviation (ESPA) programme. TheESPA programme is funded by the Department for International Development (DFID),the Economic and Social Research Council (alertESRC) and the Natural EnvironmentResearch Council (alertNERC, Project NE-J001570-1 ). See more at:http://www.espa.ac.uk/
Thanks also to my current employer, Public Health England and the National Institutefor Health Research Health Protection Research Unit (NIHR HPRU), for giving me theopportunity to be here today
