Abstract
West Nile virus (WNV) is now endemic in the continental United States; however, our ability to predict spillover transmission risk and human WNV cases remains limited. Here we develop a model depicting WNV transmission dynamics, which we optimize using a data assimilation method and two observed data streams, mosquito infection rates and reported human WNV cases. The coupled model-inference framework is then used to generate retrospective ensemble forecasts of historical WNV outbreaks in Long Island, New York for 2001–2014. Accurate forecasts of mosquito infection rates are generated before peak infection, and >65% of forecasts accurately predict seasonal total human WNV cases up to 9 weeks before the past reported case. This work provides the foundation for implementation of a statistically rigorous system for real-time forecast of seasonal outbreaks of WNV.
Highlights
West Nile virus (WNV) is endemic in the continental United States; our ability to predict spillover transmission risk and human WNV cases remains limited
In Suffolk County the seasonal sum of weekly observed infected mosquito proportions is strongly correlated with the total number of human WNV cases (r 1⁄4 0.76, P 1⁄4 0.002, Fig. 1); weekly human cases were lag correlated with mosquito infection rates during the prior week (Supplementary Fig. 1)
We developed a compartmental model that depicts the transmission dynamics of WNV among mosquitoes and birds, as well as spillover transmission to humans, and used this model to forecast the number of human WNV cases along with the peak timing, peak magnitude and total number of infectious mosquitoes over a season
Summary
West Nile virus (WNV) is endemic in the continental United States; our ability to predict spillover transmission risk and human WNV cases remains limited. The coupled model-inference framework is used to generate retrospective ensemble forecasts of historical WNV outbreaks in Long Island, New York for 2001–2014. Community-based mosquito control programs are the most effective tool to prevent the spread of WNV6 These programs are typically inadequately funded[8] and the effectiveness of these control measures can be difficult to assess due to naturally occurring confounding factors, such as host-vector interaction and susceptibility of host species[9,10,11,12,13]. A number of model-inference frameworks have been developed and used to generate accurate ensemble forecasts of infectious diseases, such as influenza and Ebola[17,18,19,20,21]. The findings indicate that accurate forecasts of WNV outcomes can be generated with considerable lead-time, and provide a foundation for a statistically rigorous system for real-time forecast of seasonal outbreaks of WNV
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