Department ofGeography
College of Earth and Mineral
Sciences
Identifying the Spatially Dynamic Variables Affecting the Distribution of West Nile Virus in Pennsylvania
GEOG – 596A, Summer 2013Mark Brady
Advisor: Dr. Justine Blanford
Department ofGeography
College of Earth and Mineral
Sciences
Acknowledge
Project Outline
BackgroundOrigin in North AmericaHealth EffectsEnzootic CycleEnvironmental Variables
MethodsGeographically Weighted Regression
Expected outcomesIdentification of Explanatory VariablesPredictive Model of WNV Distribution
Timeline
What is West Nile Virus ?
WNV was first isolated in Uganda in 1937
Appeared on the North AmericanContinent in 1999 (New York, isolated from a Flamingo in the Bronx Zoo)
WNV had spread to the west coast within 4 years
Since 1999 WNV has been detected in all of the Lower 48 States
Department ofGeography
College of Earth and Mineral
Sciences
Department ofGeography
College of Earth and Mineral
Sciences
Typical WNVTransmission
Cycle
Avian Host
Avian Host
WNV Vector
Incidental Hosts
What is West Nile Virus?
Department ofGeography
College of Earth and Mineral
Sciences
1999 2000
2001 2002
2003 2004
Why is West Nile Virus a Problem ?
Human infection with WNV may result in serious illness and in extreme cases, death
WNV is an invasive exotic species in North America
50% reduction in bird populations, particularly among Corvids (Crows and Jays)
Department ofGeography
College of Earth and Mineral
Sciences
West Nile Virus Infection - Symptoms and Prognosis
+/- 80% of people infected with WNV will develop no symptoms.
Symptoms include: fever with other symptoms such as headache, body aches, joint pains, vomiting, diarrhea, or rash, with fatigue and weakness that may last for weeks or months
< 1% of human infections are fatal (e.g. neurologic illness such as encephalitis or meningitis) and can lead to death
Department ofGeography
College of Earth and Mineral
Sciences
WNV Impacts on Human HealthDepartment ofGeography
College of Earth and Mineral
Sciences
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 20120
100020003000400050006000700080009000
10000
62 21 66
4156
9862
25393000
42693630
1356720 1021 712
5674
Human WNV Infections (1999 - 2012)Nationwide
19992000
20012002
20032004
20052006
20072008
20092010
20112012
050
100150200250300
7 2 10
284264
100 119
177
124
44 3257 43
286
Human Deaths from WNV (1999 – 2012)
Nationwide
Total Infected: 37,088
Total Deaths: 1,549
Anthropogenic and Environmental Factors Affecting WNV
Department ofGeography
College of Earth and Mineral
Sciences
Kilpatrick (2011). Globalization, Land Use, and the Invasion of West Nile Virus, Sciences
Department ofGeography
College of Earth and Mineral
Sciences
Factors Affecting WNV
Source : Reisen et al. 2006 J Med Entomol 43:309-317
Source : Blanford et al. 2012, Submitted
Temperature
Precipitation
Department ofGeography
College of Earth and Mineral
Sciences
Anthropogenic and Environmental Factors Affecting WNV - Landuse
Kilpatrick (2011).
Climate Landuse Vector Biology Host InteractionAllen et al (2009) Allen et al (2009) Andrade et al (2011) Apperson et al (2004)Andrade et al (2011) Burkett-Cadena et al (2013) Andreadis et al (2004) Boos (2009)Chaves et al (2011) Chaves et al (2011) Apperson et al (2004) Burkett-Cadena et al (2013)DeGroot et al (2008) Dale et al (2008) Blanford et al (2013) Cummins et al (2012)Deichmeister et al (2011) DeGroot et al (2008) Boos (2009) DeGroot et al (2008)Gardner et al (2012) Deichmeister et al (2011) Brinton (2002) Ghosh (2009)
Gibbs et al (2006) Ezenwa et al (2007) Burkett-Cadena et al (2013) Landesman et al (2007)
Gong et al (2011) Gibbs et al (2006) Crans (2004) Messina et al (2011)Kilpatrick et al (2006) Kilpatrick et al (2011) Chaves et al (2011) Rochlin et al (2011)Kilpatrick et al (2008) Rochlin et al (2011) Dale et al (2008) Sugumaran et al (2009)Keonraadt et al (2008) Gardner et al (2012) Weaver et al (2004)Landesman et al (2007) Hamer et al (2008)Reisen et al (2006) Kilpatrick et al (2010)Reisen et al (2010) Kilpatrick et al (2008)Ruiz et al (2010) Kwan et al (2012)Ruiz et al (2004) Reisen et al (2006)Thompson (2004) Reisen et al (2010)Trawinski et al (2008) Ruiz et al (2010)
Weaver et al (2004)
Literature Review
Department ofGeography
College of Earth and Mineral
Sciences
Factors affecting WNV
Spatial and temporal effects - Modelling at weekly/monthly/bimonthly etc. to best capture population dynamics
Land use – Urban vs. Rural
Temperature – affects virus transmission and population abundance
Rainfall – affects population abundance and availability of breeding sites
Vector species and composition (Culex species: Cx tarsalis, Cx pipiens, Cx restuans, Cx salinarius)
Challenges Modeling WNV
Environmental parameters are not stationary, they vary spatially in occurrence and intensity
The relationship between parameters influencing WNV occurrence vary spatially
The competence and abundance of vectors vary spatially
Host abundance varies spatially
Department ofGeography
College of Earth and Mineral
Sciences
Question remains… What key factors are important for predicting WNV?
Do these vary geographically?
West Nile Virus in Pennsylvania
WNV first detected in 2000
WNV PA has been collecting mosquitoes since 2000 Surveillance results used to guide mitigation efforts (larvicides, adulticides, breeding habitat removal)
Over 35,000 locations sampled statewide
Calculate MIR (Infection Rates: Proportion of mosquitoes +ve WNV of all mosquitoes collected.
Sampling sites are chosen based on nuisance complaints, past history, and staff experience
No environmental data has been collected
Department ofGeography
College of Earth and Mineral
Sciences
Project Goals and ObjectivesDepartment of
Geography
College of Earth and Mineral
Sciences
Spatial and temporal dynamics of WNV are not well described for PA since no detailed analysis of PA data has been conducted.
Explore complex interactions of a variety of factors that can influence disease dynamics.
Identify the variables that best explain the distribution and abundance of WNV in Pennsylvania using Geographically Weighted Regression (GWR)
Once identified, use the GWR model to estimate WNV distribution and intensity statewide (compared to historical, normal, and projected input criteria)
Department ofGeography
College of Earth and Mineral
Sciences
19992000
20012002
20032004
20052006
20072008
20092010
20112012
Total
0
50
100
150
200
250
0 0 3
62
237
15 259 10 14 0
286
60
Human WNV Infections (1999 - 2012)Pennsylvania
2001
2002
2003
2005
1999
2007
1999
2006
2011
2010
20122008
Department ofGeography
College of Earth and Mineral
Sciences
Culex pipiens – Primary vector of WNV to humans. Often associated with urban and suburban areas. Preferred hosts are birds, but will feed on mammals, snakes, and reptiles when avian hosts are unavailable. Larval habitats are stagnant pools, sewage plants, artificial containers (tires, buckets, etc.). Tolerant of polluted waterCulex restuans – Competent vector for WNV. Often associated with urban and suburban areas, but known to occur in diverse range of habitats. Preferred hosts are birds, but will feed on mammals, amphibians, and reptiles when avian hosts are unavailable. Larval habitats are similar to Cx. Pipiens, but less tolerant of polluted water. Abundant early in season and amplification of WNV.
Culex salinarius – An opportunistic feeder that will readily feed on birds or mammals, therefore may be an important bridge vector for WNV. Larval habitats include temporary grassy pools and artificial containers, though this species prefers natural habitats to artificial habitats.
Important WNV Vector Species in Pennsylvania
Identify the variables that most affect the abundance, competence, and distribution of WNV in PA
Overview of WNV in PA Analyze 6 years of data:
2003 and 2012 (high WNV incidence)2006 and 2007 (mid WNV incidence)2001 and 2011 (low WNV incidence)
Identify key WNV locations over the years
Identify temporal patterns of WNV (seasonality)
Describe vector populations (spatial, temporal, species)
Describe vector competence (spatial, temporal, species)Explore spatially varying relationships between WNV variables using GWR
Department ofGeography
College of Earth and Mineral
Sciences
Proposed Methods
Department ofGeography
College of Earth and Mineral
SciencesTemperature – Min, Max, Mean, Duration
Precipitation – Weekly/Monthly Sums and Means
Land Uses – Percentages by Spatial Units
Human Population Densities by Spatial Units
Vectors – Populations and Distributions by Temporal and Spatial Units
MIR – Mosquito Infection Rates
Potentially Significant Variables
Department ofGeography
College of Earth and Mineral
Sciences
Data Sources
Landuse PopulationCadastral UnitsWatershed BoundariesHydrography
Precipitation Temperature Climate Normal SummariesClimate Forecasts
Vector ID Vector EnumerationsWNV Test ResultsHistorical/Future Treatments
Proposed Methodology:Geographically Weighted Regression (GWR)
Brunsdon, Fotheringham, and Charlton (1996)Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity
Spatial Autocorrelation
Tobler’s Law (1970)
Extension of multivariate regression that allows regression models to vary spatially
Allows the relationships between the independent variables to vary
Department ofGeography
College of Earth and Mineral
Sciences
Department ofGeography
College of Earth and Mineral
Sciences
Proposed Methodology:Geographically Weighted Regression (GWR)
y = β +β x +ε 0 1 for i=1 … n
Department ofGeography
College of Earth and Mineral
Sciences 𝑦𝑖 = 𝑏0 + 𝑏𝑗𝑚
𝑗=1 𝑥𝑖𝑗 + 𝜀𝑖
𝒚= 𝑿𝒃+ 𝒆
𝑦𝑖⋮𝑦𝑛൩ = 𝑥11 ⋯ 𝑥1𝑚⋮ ⋱ ⋮𝑥𝑛1 ⋯ 𝑥𝑛𝑚൩ 𝑏0⋮𝑏𝑚൩ + 𝜀1⋮𝜀𝑛൩
𝒃= ሺ𝑿𝑻𝑿ሻ –𝟏𝑿𝑻 𝒚 𝒃= ሺ𝑿𝑻𝑾𝑿ሻ –𝟏𝑾𝑿𝑻 𝒚
Proposed Methodology:Geographically Weighted Regression (GWR)
b = Regression Coefficientsy = Variable EstimatesW = Weighting Coefficients
Department ofGeography
College of Earth and Mineral
Sciences
Proposed Methodology:Geographically Weighted Regression (GWR)
Kernel Function -Defines the shapeof the spatial weightingfunction (w)
W = 1
W = 0 D
*ArcMap uses a Gaussian function
Fixed Bandwidth
Adaptive Bandwidth
Department ofGeography
College of Earth and Mineral
Sciences
Proposed Methodology:Geographically Weighted Regression (GWR)
Output feature class (estimates at regression points)
Model coefficient rasters for each variable
Diagnostic summary table
Prediction output feature class (estimates at locations other than regression points)
Department ofGeography
College of Earth and Mineral
Sciences
Proposed Methodology:Geographically Weighted Regression (GWR)
Department ofGeography
College of Earth and Mineral
Sciences
Annual Land useTemperature (Mean)Population DensityWNV +ve mosquitoes
Adaptive Bandwidth
2003 Land useTemperature (Mean)Population DensityWNV +ve mosquitoes
Fixed Bandwidth
Exploratory Analysis and Results
Regression Coefficients
Department ofGeography
College of Earth and Mineral
Sciences
Annual Land useTemperature (Mean)Population DensityWNV +ve mosquitoes
Adaptive Bandwidth
2003 Land useTemperature (Mean)Population DensityWNV +ve mosquitoes
Fixed Bandwidth
Exploratory Analysis and Results
Estimate Standard Residuals
Department ofGeography
College of Earth and Mineral
Sciences
Annual Land useTemperature (Mean)Population DensityWNV +ve mosquitoes
Adaptive Bandwidth
2003 Land useTemperature (Mean)Population DensityWNV +ve mosquitoes
Fixed Bandwidth
Exploratory Analysis and Results
Estimate Residuals
Department ofGeography
College of Earth and Mineral
Sciences
Annual Land useTemperature (Mean)Population DensityWNV +ve mosquitoes
Adaptive Bandwidth
2003 Land useTemperature (Mean)Population DensityWNV +ve mosquitoes
Fixed Bandwidth
Exploratory Analysis and Results
Local R2 Statistic
Department ofGeography
College of Earth and Mineral
Sciences
Annual LanduseTemperature (Mean)Population DensityWNV +ve mosquitoes
Adaptive Bandwidth
2003 LanduseTemperature (Mean)Population DensityWNV +ve mosquitoes
Fixed Bandwidth
Exploratory Analysis and Results
Statistical Summary Tables
Department ofGeography
College of Earth and Mineral
Sciences
Acknowledge
Expected Results
A dataset of historical mosquito populations, competence, and species distribution merged with potentially relevant environmental data
Identify the environmental variables best suited to explain the historical distribution and intensity of WNV in PA
Develop a predictive GWR model using historical relationships between environmental variables and mosquito vectors, in order to estimate WNV response to future changes in climate, landuse, and human population dynamics
Department ofGeography
College of Earth and Mineral
Sciences
Project Timeline
May2013
January2014
February2014
July2013
March2014
596 A Literature
review
596 B Complete
Data Analysis
596 A Peer Review
Cloud Server Class
Conference Presentation
Department ofGeography
College of Earth and Mineral
Sciences
Selected References Blanford, J. I., Blanford, S., Crane, R. G., Mann, M. E., Paaijmans, K. P., Schreiber, K. V., et al. (2013). Implications of temperature variation for malaria parasite development across Africa. Scientific Reports , 3 (1300).
Brunsdon, C., Fotheringham, A. S., & Charlton, M. (1999). Some notes on parametric significance tests for geographically weighted regression. Journal of Regional Science , 39 (3), 497-524.
Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical Analysis , 28 (4), 281-298.
Brunsdon, C., McClatchey, J., & Unwin, D. J. (2001). Spatial variation in the average rainfall - altitude relationship in Great Britain: an approach using geographically weighted regression. International Journal of Climatology , 21, 455-456.
Charlton, M., & Fotheringham, A. S. (2009). Geographically Weighted Regression (White Paper). National University of Ireland Maynooth. Maynooth, Ireland: National Center for Geocomputation.
PAWNVCP. (2013). Pennsylvania's West Nile Virus Control Program. Retrieved May 18, 2013, from http://www.westnile.state.pa.us/index.html
Reisen, W. K., Fang, Y., & Martinez, V. M. (2006). Effects of temperature on the transmission of West Nile virus by Culex tarsalis (Diptera:Culicidae). Journal of Medical Entomology , 43 (2), 309-317.
Department ofGeography
College of Earth and Mineral
Sciences
Selected References Reisen, W. K., Thiemann, T., Barker, C. M., Lu, H., Carroll, B., Fang, Y., et al. (2010). Effects of warm winter temperature on the abundance and gonotrophic activity of Culex (Diptera:Culicidae) in California. Journal of Medical Entomology , 47 (2), 230-237.
Ruiz, M. O., Tedesco, C., McTighe, T. J., Austin, C., & Kitron, U. (2004). Environmental and social determinants of human risk during a West Nile virus outbreak in the greater Chicago area, 2002. International Journal of Health Geographics , 3 (8).
Kilpatrick, A. M. (2011). Globalization, Land Use, and the Invasion of West Nile Virus. Science , 334, 323-327.Kilpatrick, A. M., Daszak, P., Jones, M. J., Peter, P. M., & Kramer, L. D. (2006). Host heterogeneity dominates West Nile virus transmission. Proc Biol Sci , 273, 2327-2333.
Kilpatrick, A. M., Fornseca, D. M., Ebel, G. D., Reddy, M. R., & Kramer, L. D. (2010). Spatial and temporal variation in vector competence of Culex pipiens and Culex restuans mosquitoes for West Nile virus. Am J Trop Med Hyg , 83 (3), 607-613.
Kilpatrick, A. M., Meola, M. A., Robin, M. M., & Kramer, L. D. (2008). Temperature, viral genetics, and the transmission of West Nile virus by Culex pipiens mosquitoes. Plos Pathogens , 4 (6).
Chaves, L. F., Hamer, G. L., Walker, E. D., Brown, W. M., Ruiz, M. O., & Kitron, U. D. (2011). Climatic variability and landscape heterogeneity impact urban mosquito diversity and vector abundance and infection. Ecosphere , 2 (6).
Department ofGeography
College of Earth and Mineral
Sciences
Acknowledgements Dr. Justine Blanford
Michael Hutchinson - PA West Nile Virus Control Program
Andrew Kyle - PA West Nile Virus Control Program
James Haefner - PA West Nile Virus Control Program
Matt Helwig - PA West Nile Virus Control Program
Dr. Doug Miller
Beth King
Department ofGeography
College of Earth and Mineral
Sciences
Questions