Citation:

Myer, M. AND JohnM Johnston. Spatiotemporal Bayesian modeling of West Nile virus: Identifying risk of infection in mosquitoes with local-scale predictors. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, Netherlands, 650(2):2818-2829, (2019). https://doi.org/10.1016/j.scitotenv.2018.09.397

Impact/Purpose:

Monitoring and control of rare but fatal arboviral diseases such as West Nile virus presents a challenge to state and local vector control managers. In a prior study of neighboring Suffolk County, modeling mosquito presence and viral incidence revealed weather (temperature and rainfall) as reliable predictors. These predictors are consistent with other studies and important regionally. However, variations in land use type and mosquito autecology introduce unique dynamics of disease spread at the scale of a county or city. Nassau County, New York is a highly developed county on Long Island near New York City, with a well-established arboviral surveillance network and vector-borne disease prevention program. We applied Bayesian spatiotemporal modeling using R-INLA to fit a predictive model and evaluate ecological and sociological predictors of West Nile virus incidence, developing a method that can be used to identify locally-influential predictors from amongst dozens of potential covariates. We provide a method for improving spatiotemporal models of West Nile virus incidence for decision making at the county and community scale, which empowers disease and vector control organizations to prioritize and evaluate prevention efforts.

Description:

Monitoring and control of West Nile virus (WNV) presents a challenge to state and local vector control managers. Models of mosquito presence and viral incidence have revealed that variations in mosquito autecology and land use patterns introduce unique dynamics of disease at the scale of a county or city, and that effective prediction requires locally parameterized models. We applied Bayesian spatiotemporal modeling to West Nile surveillance data from 49 mosquito trap sites in Nassau County, New York, from 2001 to 2015 and evaluated environmental and sociological predictors of West Nile virus incidence in Culex pipiens-restuans. A Bayesian spike-and-slab variable selection algorithm was used to help select influential independent variables. This method can be used to identify locally-important predictors. The best model predicted West Nile positives well, with an Area Under Curve (AUC) of 0.83 on holdout data. The temporal trend was nonlinear and increased throughout the year. The spatial component identified increased West Nile incidence odds in the northwestern portion of the county, with lower odds in wetlands on the south shore of Long Island. High Normalized Difference Vegetation Index (NDVI) areas, wetlands, and areas of high urban development had negative associations with WNV incidence. In this study we demonstrate a method for improving spatiotemporal models of West Nile virus incidence for decision making at the county and community scale, which empowers disease and vector control organizations to prioritize and evaluate prevention efforts.

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