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Toward Ecological Forecasting of West Nile Virus in Florida: Insights from Two Decades of Surveillance

Toward Ecological Forecasting of West Nile Virus in Florida: Insights from Two Decades of Surveillance

This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint.

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Authors

Joseph Alex Baecher , V A Askshay, Robert Guralnick, Amy M Bauer, Yasmin N Tavares, Yesenia Sánchez, James T Thorson, Lindsay P Campbell

Abstract

West Nile Virus (WNV) is the leading cause of mosquito-borne disease in the United States, yet transmission activity remains difficult to predict. This study used 20 years of digitized WNV seroconversion data from 526 sentinel chicken coops across Florida to develop spatiotemporal models with landscape and climate variables to predict WNV seroconversion at monthly and seasonal timescales. We found several environmental predictors hypothesized to impact WNV transmission were important at both timescales. Lower WNV seroconversion was predicted with higher maximum temperature during the sampling month and greater proportions of developed land cover, while intermediate values of minimum temperature at two-months prior predicted higher WNV seroconversion. In the seasonal model, intermediate values of cumulative precipitation one season prior predicted higher WNV seroconversion. High accuracy in out-of-sample predictions at both timescales demonstrates the utility of our models toward ecological forecasting of enzootic transmission. Monthly models had higher precision than the seasonal model, but both timescales have potential to inform management decisions. Monthly predictions could guide targeted control efforts during active transmission seasons, while seasonal predictions provide a lead-time to improve preparedness and inform resource allocation. Retrospective statewide predictions across the 20 year time period provided qualitative correlations between areas of high predicted WNV transmission hazard among humans and equines, while also providing insights into WNV transmission ecology following its introduction in 2001. Overall, our framework provides a step forward in the use of spatiotemporal ecological modeling for public health and vector-borne disease ecology and management.

DOI

https://doi.org/10.32942/X2QH09

Subjects

Environmental Monitoring, Medicine and Health Sciences, Physical Sciences and Mathematics, Statistical Models, Virus Diseases

Keywords

vector-borne disease, arbovirus, sentinel chickens, spatiotemporal modeling, 21 Gaussian Markov random fields, stochastic partial differential equations, Gaussian Markov random fields

Dates

Published: 2025-05-20 10:49

Last Updated: 2025-05-20 10:49

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License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
University of Florida Research Opportunity Seed Fund

Data and Code Availability Statement:
All code necessary to conduct these analyses are stored in the 489 following Github repository: https://github.com/slamander/chickens. Georeferenced sentinel 490 chicken seroconversion data is available upon request through the Florida Department of Health 491 Arbovirus Surveillance program upon agreement from participating Florida mosquito control 492 programs through a memorandum of understanding. The authors did not receive special 493 privileges in accessing the data that other researchers would not have. Contact information for 494 data requests are available through the FDOH website: https://www.floridahealth.gov/diseases-495 and-conditions/mosquito-borne-diseases/surveillance.html.

Language:
English