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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

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slide1

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

slide2

Department of

Geography

College of Earth and Mineral Sciences

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

Acknowledge

slide3

Department of

Geography

College of Earth and Mineral Sciences

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

slide4

What is West Nile Virus?

Department of

Geography

College of Earth and Mineral Sciences

Avian Host

Typical WNV

Transmission

Cycle

Incidental Hosts

WNV Vector

Avian Host

slide5

1999

2000

Department of

Geography

College of Earth and Mineral Sciences

2001

2002

2003

2004

slide6

Department of

Geography

College of Earth and Mineral Sciences

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)

slide7

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

slide8

WNV Impacts on Human Health

Department of

Geography

College of Earth and Mineral Sciences

Total Infected: 37,088

Total Deaths: 1,549

slide9

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

slide10

Factors Affecting WNV

Department of

Geography

College of Earth and Mineral Sciences

Temperature

Source : Reisen et al. 2006 J Med Entomol 43:309-317

Precipitation

Source : Blanford et al. 2012, Submitted

slide11

Anthropogenic and Environmental Factors Affecting WNV - Landuse

Department of

Geography

College of Earth and Mineral Sciences

Kilpatrick (2011).

slide13

Factors affecting WNV

Department of

Geography

College of Earth and Mineral Sciences

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)

slide14

Department of

Geography

College of Earth and Mineral Sciences

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

Question remains…

What key factors are important for predicting WNV?

Do these vary geographically?

slide15

West Nile Virus in Pennsylvania

Department of

Geography

College of Earth and Mineral Sciences

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

slide16

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)

slide17

Department of

Geography

College of Earth and Mineral Sciences

2001

2005

1999

2002

2006

2010

2003

2007

2011

1999

2008

2012

slide18

Important WNV Vector Species in Pennsylvania

Department of

Geography

College of Earth and Mineral Sciences

Culexpipiens –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

Culexrestuans – 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.

Culexsalinarius – 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.

slide19

Proposed Methods

Department of

Geography

College of Earth and Mineral Sciences

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

slide20

Department of

Geography

College of Earth and Mineral Sciences

Potentially Significant Variables

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

slide21

Data Sources

Department of

Geography

College of Earth and Mineral Sciences

Landuse

Population

Cadastral Units

Watershed Boundaries

Hydrography

Precipitation

Temperature

Climate Normal Summaries

Climate Forecasts

Vector ID

Vector Enumerations

WNV Test Results

Historical/Future Treatments

slide22

Proposed Methodology:

Geographically Weighted Regression (GWR)

Department of

Geography

College of Earth and Mineral Sciences

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

slide23

Proposed Methodology:

Geographically Weighted Regression (GWR)

Department of

Geography

College of Earth and Mineral Sciences

slide24

ProposedMethodology:

Geographically Weighted Regression (GWR)

Department of

Geography

College of Earth and Mineral Sciences

y = β +β x +ε 0 1 for i=1 … n

b= Regression Coefficients

y = Variable Estimates

W= Weighting Coefficients

slide25

Proposed Methodology:

Geographically Weighted Regression (GWR)

Department of

Geography

College of Earth and Mineral Sciences

Kernel Function -

Defines the shape

of the spatial weighting

function (w)

W = 1

W = 0

D

*ArcMap uses a Gaussian function

slide26

Proposed Methodology:

Geographically Weighted Regression (GWR)

Department of

Geography

College of Earth and Mineral Sciences

Adaptive Bandwidth

Fixed Bandwidth

slide27

Proposed Methodology:

Geographically Weighted Regression (GWR)

Department of

Geography

College of Earth and Mineral Sciences

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)

slide28

Exploratory Analysis and Results

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

Regression Coefficients

slide29

Exploratory Analysis and Results

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

Estimate Standard Residuals

slide30

Exploratory Analysis and Results

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

Estimate Residuals

slide31

Exploratory Analysis and Results

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

Local R2 Statistic

slide32

Exploratory Analysis and Results

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

Statistical Summary Tables

slide33

Expected Results

Department of

Geography

College of Earth and Mineral Sciences

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

Acknowledge

slide34

Department of

Geography

College of Earth and Mineral Sciences

Project Timeline

Conference Presentation

596 A Peer Review

March

2014

596 A Literature review

February

2014

January

2014

596 B Complete Data Analysis

July

2013

May

2013

Cloud Server Class

slide35

Selected References

Department of

Geography

College of Earth and Mineral Sciences

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.

slide36

Selected References

Department of

Geography

College of Earth and Mineral Sciences

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).

slide37

Acknowledgements

Department of

Geography

College of Earth and Mineral Sciences

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

slide38

Questions

Department of

Geography

College of Earth and Mineral Sciences