Spatiotemporal Bayesian modeling of West Nile virus: Identifying risk of infection in mosquitoes with local-scale predictors

• Published in: The Science of the total environment 2019-02, Vol.650 (Pt 2), p.2818-2829
• Main Authors: Myer, Mark H., Johnston, John M.
• Format: Article
• Language: English
• Subjects: Animals; Arboviral; Bayes Theorem; Bayesian; Culex - virology; Local variables; Models, Theoretical; Mosquito Vectors - virology; New York; Seasons; Spatiotemporal; Vector-borne disease; West Nile; West Nile virus - physiology
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2018.09.397

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. [Display omitted] •49 mosquito trap locations tested for West Nile virus over a 15-year span•Bayesian zero-inflated binomial model with spatial and temporal correlated random effects developed using R-INLA•Model predicted 75% of all WNV outcomes correctly on holdout data, with 72% of positives correctly classified.•Spatial and temporal trends mapped across the study area highlighted areas and times of concern for WNV incidence.•Areas with lower vegetation and less-dense development, such as suburbs, were at highest risk.