Ensemble forecast of human West Nile virus cases and mosquito infection rates

Nicholas B. DeFelice (), Eliza Little, Scott R. Campbell and Jeffrey Shaman
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Nicholas B. DeFelice: Mailman School of Public Health, Columbia University
Eliza Little: Mailman School of Public Health, Columbia University
Scott R. Campbell: Arthropod-Borne Disease Laboratory
Jeffrey Shaman: Mailman School of Public Health, Columbia University

Nature Communications, 2017, vol. 8, issue 1, 1-6

Abstract: 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.

Date: 2017
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DOI: 10.1038/ncomms14592

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