Department of Geography College of Earth and Mineral Sciences Identifying the Spatially Dynamic Variables Affecting the Distribution of West Nile Virus in Pennsylvania GEOG – 596A, Summer 2013 Mark Brady Advisor: Dr. Justine Blanford

Department of Geography College of Earth and Mineral Sciences Acknowledge Project Outline Background Origin in North America Health Effects Enzootic Cycle Environmental Variables Methods Geographically Weighted Regression Expected outcomes Identification of Explanatory Variables Predictive Model of WNV Distribution Timeline

What is West Nile Virus ? WNV was first isolated in Uganda in 1937 Appeared on the North American Continent 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 of Geography College of Earth and Mineral Sciences

Department of Geography College of Earth and Mineral Sciences Typical WNV Transmission Cycle Avian Host WNV Vector Incidental Hosts What is West Nile Virus?

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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 of Geography 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 of Geography College of Earth and Mineral Sciences

WNV Impacts on Human Health Department of Geography College of Earth and Mineral Sciences Total Infected: 37,088 Total Deaths: 1,549

Anthropogenic and Environmental Factors Affecting WNV Department of Geography College of Earth and Mineral Sciences Kilpatrick (2011). Globalization, Land Use, and the Invasion of West Nile Virus, Sciences

Department of Geography College of Earth and Mineral Sciences Factors Affecting WNV Source : Reisen et al J Med Entomol 43: Source : Blanford et al. 2012, Submitted Temperature Precipitation

Department of Geography College of Earth and Mineral Sciences Anthropogenic and Environmental Factors Affecting WNV - Landuse Kilpatrick (2011).

ClimateLanduseVector BiologyHost Interaction 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) 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 of Geography 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 of Geography 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 of Geography College of Earth and Mineral Sciences

Project Goals and Objectives Department 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)

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Department of Geography 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 water Culex 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 of Geography College of Earth and Mineral Sciences Proposed Methods

Department of Geography College of Earth and Mineral Sciences Temperature – 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 of Geography College of Earth and Mineral Sciences Data Sources Landuse Population Cadastral Units Watershed Boundaries Hydrography Precipitation Temperature Climate Normal Summaries Climate Forecasts Vector ID Vector Enumerations WNV Test Results Historical/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 of Geography College of Earth and Mineral Sciences

Department of Geography College of Earth and Mineral Sciences Proposed Methodology: Geographically Weighted Regression (GWR)

y  x  0 1 for i=1 … n Department of Geography College of Earth and Mineral Sciences Proposed Methodology : Geographically Weighted Regression (GWR) b = Regression Coefficients y = Variable Estimates W = Weighting Coefficients

Department of Geography College of Earth and Mineral Sciences Proposed Methodology: Geographically Weighted Regression (GWR) Kernel Function - Defines the shape of the spatial weighting function ( w ) W = 1 W = 0 D *ArcMap uses a Gaussian function

Fixed Bandwidth Adaptive Bandwidth Department of Geography 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 of Geography College of Earth and Mineral Sciences Proposed Methodology: Geographically Weighted Regression (GWR)

Department of Geography College of Earth and Mineral Sciences Annual Land use Temperature (Mean) Population Density WNV +ve mosquitoes Adaptive Bandwidth 2003 Land use Temperature (Mean) Population Density WNV +ve mosquitoes Fixed Bandwidth Exploratory Analysis and Results Regression Coefficients

Department of Geography College of Earth and Mineral Sciences Annual Land use Temperature (Mean) Population Density WNV +ve mosquitoes Adaptive Bandwidth 2003 Land use Temperature (Mean) Population Density WNV +ve mosquitoes Fixed Bandwidth Exploratory Analysis and Results Estimate Standard Residuals

Department of Geography College of Earth and Mineral Sciences Annual Land use Temperature (Mean) Population Density WNV +ve mosquitoes Adaptive Bandwidth 2003 Land use Temperature (Mean) Population Density WNV +ve mosquitoes Fixed Bandwidth Exploratory Analysis and Results Estimate Residuals

Department of Geography College of Earth and Mineral Sciences Annual Land use Temperature (Mean) Population Density WNV +ve mosquitoes Adaptive Bandwidth 2003 Land use Temperature (Mean) Population Density WNV +ve mosquitoes Fixed Bandwidth Exploratory Analysis and Results Local R 2 Statistic

Department of Geography College of Earth and Mineral Sciences Annual Landuse Temperature (Mean) Population Density WNV +ve mosquitoes Adaptive Bandwidth 2003 Landuse Temperature (Mean) Population Density WNV +ve mosquitoes Fixed Bandwidth Exploratory Analysis and Results Statistical Summary Tables

Department of Geography 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 of Geography College of Earth and Mineral Sciences Project Timeline May 2013 January 2014 February 2014 July 2013 March A Literature review 596 B Complete Data Analysis 596 A Peer Review Cloud Server Class Conference Presentation

Department of Geography 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), Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical Analysis, 28 (4), 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, 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 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),

Department of Geography 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), 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, International Journal of Health Geographics, 3 (8). Kilpatrick, A. M. (2011). Globalization, Land Use, and the Invasion of West Nile Virus. Science, 334, 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, 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), 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 of Geography College of Earth and Mineral Sciences Acknowledgements

Department of Geography College of Earth and Mineral Sciences Questions