Development and validation of a climate-based ensemble prediction model for West Nile Virus infection rates in Culex mosquitoes, Suffolk County, New York.

Eliza Little,Scott R Campbell,Jeffrey Shaman

doi:10.1186/s13071-016-1720-1

Eliza Little, Scott R Campbell + Show 1 more

Open Access

https://doi.org/10.1186/s13071-016-1720-1

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Abstract

BackgroundWest Nile Virus (WNV) is an endemic public health concern in the United States that produces periodic seasonal epidemics. Underlying these outbreaks is the enzootic cycle of WNV between mosquito vectors and bird hosts. Identifying the key environmental conditions that facilitate and accelerate this cycle can be used to inform effective vector control.ResultsHere, we model and forecast WNV infection rates among mosquito vectors in Suffolk County, New York using readily available meteorological and hydrological conditions. We first validate a statistical model built with surveillance data between 2001 and 2009 (m09) and specify a set of new statistical models using surveillance data from 2001 to 2012 (m12). This ensemble of new models is then used to make predictions for 2013–2015, and multimodel inference is employed to provide a formal probabilistic interpretation across the disparate individual model predictions. The findings of the m09 and m12 models align; with the ensemble of m12 models indicating an association between warm, dry early spring (April) conditions and increased annual WNV infection rates in Culex mosquitoes.ConclusionsThis study shows that real-time climate information can be used to predict WNV infection rates in Culex mosquitoes prior to its seasonal peak and before WNV spillover transmission risk to humans is greatest.Electronic supplementary materialThe online version of this article (doi:10.1186/s13071-016-1720-1) contains supplementary material, which is available to authorized users.

Highlights

West Nile Virus (WNV) is an endemic public health concern in the United States that produces periodic seasonal epidemics
Among the model forms tested, a mixed effects negative binomial (MENB) model with grid cell as a random effect resulted in the best fitting model by the Akaike Information Criterion, AIC
We found a wide range of predicted WNV infection rates between models (Additional file 1) indicating need for a formal probabilistic interpretation across predictions

Summary

Introduction

West Nile Virus (WNV) is an endemic public health concern in the United States that produces periodic seasonal epidemics. Underlying these outbreaks is the enzootic cycle of WNV between mosquito vectors and bird hosts. West Nile Virus (WNV), first introduced in North America in New York during 1999, quickly spread across the United States. WNV in the US is maintained by an enzootic cycle driven by virus transmission between avian reservoir hosts and bird-biting mosquito vectors. 65 mosquito species have been found infected with WNV in the US [4]; only a few of these species are likely important in the transmission of WNV. In the northeastern US, Culex pipiens and Culex restuans are the suspected enzootic vectors while Culex pipiens and Culex salinarius are the main epidemic vectors [5, 6]; Little et al Parasites & Vectors (2016) 9:443 the critical vector(s) may change over a season and may not be fully enumerated [6]

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