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Seasonal temperatures and hydrological conditions improve the prediction of West Nile virus infection rates in Culex mosquitoes and human case counts in New York and Connecticut

Author

Listed:
  • Alexander C Keyel
  • Oliver Elison Timm
  • P Bryon Backenson
  • Catharine Prussing
  • Sarah Quinones
  • Kathleen A McDonough
  • Mathias Vuille
  • Jan E Conn
  • Philip M Armstrong
  • Theodore G Andreadis
  • Laura D Kramer

Abstract

West Nile virus (WNV; Flaviviridae: Flavivirus) is a widely distributed arthropod-borne virus that has negatively affected human health and animal populations. WNV infection rates of mosquitoes and human cases have been shown to be correlated with climate. However, previous studies have been conducted at a variety of spatial and temporal scales, and the scale-dependence of these relationships has been understudied. We tested the hypothesis that climate variables are important to understand these relationships at all spatial scales. We analyzed the influence of climate on WNV infection rate of mosquitoes and number of human cases in New York and Connecticut using Random Forests, a machine learning technique. During model development, 66 climate-related variables based on temperature, precipitation and soil moisture were tested for predictive skill. We also included 20–21 non-climatic variables to account for known environmental effects (e.g., land cover and human population), surveillance related information (e.g., relative mosquito abundance), and to assess the potential explanatory power of other relevant factors (e.g., presence of wastewater treatment plants). Random forest models were used to identify the most important climate variables for explaining spatial-temporal variation in mosquito infection rates (abbreviated as MLE). The results of the cross-validation support our hypothesis that climate variables improve the predictive skill for MLE at county- and trap-scales and for human cases at the county-scale. Of the climate-related variables selected, mean minimum temperature from July–September was selected in all analyses, and soil moisture was selected for the mosquito county-scale analysis. Models demonstrated predictive skill, but still over- and under-estimated WNV MLE and numbers of human cases. Models at fine spatial scales had lower absolute errors but had greater errors relative to the mean infection rates.

Suggested Citation

  • Alexander C Keyel & Oliver Elison Timm & P Bryon Backenson & Catharine Prussing & Sarah Quinones & Kathleen A McDonough & Mathias Vuille & Jan E Conn & Philip M Armstrong & Theodore G Andreadis & Laur, 2019. "Seasonal temperatures and hydrological conditions improve the prediction of West Nile virus infection rates in Culex mosquitoes and human case counts in New York and Connecticut," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-32, June.
    • Handle: RePEc:plo:pone00:0217854
    DOI: 10.1371/journal.pone.0217854
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    References listed on IDEAS

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    1. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
    2. Shannon L. LaDeau & A. Marm Kilpatrick & Peter P. Marra, 2007. "West Nile virus emergence and large-scale declines of North American bird populations," Nature, Nature, vol. 447(7145), pages 710-713, June.
    3. 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.
    4. Ryan J Harrigan & Henri A Thomassen & Wolfgang Buermann & Robert F Cummings & Matthew E Kahn & Thomas B Smith, 2010. "Economic Conditions Predict Prevalence of West Nile Virus," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-8, November.
    5. Joe Whittaker, 1984. "Model Interpretation from the Additive Elements of the Likelihood Function," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 33(1), pages 52-64, March.
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