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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Nicholas B. DeFelice & Eliza Little & Scott R. Campbell & Jeffrey Shaman, 2017. "Ensemble forecast of human West Nile virus cases and mosquito infection rates," Nature Communications, Nature, vol. 8(1), pages 1-6, April.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms14592
    DOI: 10.1038/ncomms14592
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    Cited by:

    1. Sifat A Moon & Lee W Cohnstaedt & D Scott McVey & Caterina M Scoglio, 2019. "A spatio-temporal individual-based network framework for West Nile virus in the USA: Spreading pattern of West Nile virus," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-24, March.
    2. Nicholas B DeFelice & Zachary D Schneider & Eliza Little & Christopher Barker & Kevin A Caillouet & Scott R Campbell & Dan Damian & Patrick Irwin & Herff M P Jones & John Townsend & Jeffrey Shaman, 2018. "Use of temperature to improve West Nile virus forecasts," PLOS Computational Biology, Public Library of Science, vol. 14(3), pages 1-25, March.
    3. Nguyen, Ngoc T. & Bish, Ebru K. & Bish, Douglas R., 2021. "Optimal pooled testing design for prevalence estimation under resource constraints," Omega, Elsevier, vol. 105(C).
    4. Surendra Karki & William M Brown & John Uelmen & Marilyn O’Hara Ruiz & Rebecca Lee Smith, 2020. "The drivers of West Nile virus human illness in the Chicago, Illinois, USA area: Fine scale dynamic effects of weather, mosquito infection, social, and biological conditions," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-19, May.

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