Title: Spatiotemporal modeling of ecological and sociological predictors of West Nile virus in Suffolk County, NY, mosquitoes

Abstract

Abstract Suffolk County, New York, is a locus for West Nile virus ( WNV ) infection in the American northeast that includes the majority of Long Island to the east of New York City. The county has a system of light and gravid traps used for mosquito collection and disease monitoring. In order to identify predictors of WNV incidence in mosquitoes and predict future occurrence of WNV , we have developed a spatiotemporal Bayesian model, beginning with over 40 ecological, meteorological, and built‐environment covariates. A mixed‐effects model including spatially and temporally correlated errors was fit to WNV surveillance data from 2008 to 2014 using the R package “R‐INLA,” which allows for Bayesian modeling using the stochastic partial differential equation ( SPDE ) approach. The integrated nested Laplace approximation ( INLA ) SPDE allows for simultaneous fitting of a temporal parameter and a spatial covariance, while incorporating a variety of likelihood functions and running in R statistical software on a home computer. We found that land cover classified as open water and woody wetlands had a negative association with WNV incidence in mosquitoes, and the count of septic systems was associated with an increase in WNV . Mean temperature at two‐week lagmore » was associated with a strong positive impact, while mean precipitation at no lag and one‐week lag was associated with positive and negative impacts on WNV , respectively. Incorporation of spatiotemporal factors resulted in a marked increase in model goodness‐of‐fit. The predictive power of the model was evaluated on 2015 surveillance results, where the best model achieved a sensitivity of 80.9% and a specificity of 77.0%. The spatial covariate was mapped across the county, identifying a gradient of WNV prevalence increasing from east to west. The Bayesian spatiotemporal model improves upon previous approaches, and we recommend the INLA SPDE methodology as an efficient way to develop robust models from surveillance data to develop and enhance monitoring and control programs. Our study confirms previously found associations between weather conditions and WNV and suggests that wetland cover has a mitigating effect on WNV infection in mosquitoes, while high septic system density is associated with an increase in WNV infection.« less

Authors:

Myer, Mark H. ^[1]; Campbell, Scott R. ^[2]; Johnston, John M. ^[3]

---

+ Show Author Affiliations

1. Oak Ridge Institute for Science and Education, Office of Research and Development National Exposure Research Laboratory U.S. Environmental Protection Agency 960 College Station Road Athens Georgia 30605 USA
2. Arthropod‐Borne Disease Laboratory Suffolk County Department of Health Services Yaphank New York 11980 USA
3. Office of Research and Development National Exposure Research Laboratory U.S. Environmental Protection Agency 960 College Station Road Athens Georgia 30605 USA

Publication Date:

Thu Jun 15 00:00:00 EDT 2017

Research Org.:

Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN (United States); US Environmental Protection Agency, Athens, GA (United States)

Sponsoring Org.:

USDOE; USEPA

OSTI Identifier:

1363967

Alternate Identifier(s):

OSTI ID: 1367355; OSTI ID: 1393829

Grant/Contract Number:  

DW8992298301

Resource Type:

Published Article

Journal Name:

Ecosphere

Additional Journal Information:

Journal Name: Ecosphere Journal Volume: 8 Journal Issue: 6; Journal ID: ISSN 2150-8925

Publisher:

Wiley Blackwell (John Wiley & Sons)

Country of Publication:

United States

Language:

English

Subject:

54 ENVIRONMENTAL SCIENCES; 60 APPLIED LIFE SCIENCES; 59 BASIC BIOLOGICAL SCIENCES; Bayesian; Culex pipiens; disease ecology; integrated nested Laplace approximation (INLA); Long Island; septic systems; spatial modeling; spatiotemporal modeling; stochastic partial differential equation (SPDE); Suffolk; West Nile