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Using Climate to Explain and Predict West Nile Virus Risk in Nebraska.

Kelly Helm Smith1, Andrew J Tyre2, Jeff Hamik3

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Predicting West Nile Virus (WNV) risk is possible using climate data. Warm, dry conditions following wet years significantly increase WNV cases, offering a basis for early warning systems.

Keywords:
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Area of Science:

  • Environmental Science
  • Epidemiology
  • Climate Science

Background:

  • West Nile Virus (WNV) poses a significant public health concern.
  • Predictive models for WNV outbreaks are crucial for timely intervention.
  • Climate variability is increasingly recognized as a driver of vector-borne disease dynamics.

Purpose of the Study:

  • To develop and validate a predictive model for West Nile Virus (WNV) risk in Nebraska.
  • To identify key climatic factors and their temporal lags influencing WNV incidence.
  • To quantify the potential impact of climate conditions on WNV case numbers.

Main Methods:

  • Utilized monthly precipitation and temperature data from 2002-2018.
  • Employed generalized additive models with negative binomial distribution and smoothing curves.
  • Tested various combinations of temperature and drought data, lagged from 12 to 36 months.
  • Validated model performance using out-of-sample prediction and compared against a naïve model.

Main Results:

  • Warm temperatures and a dry year preceded by a wet year were identified as the strongest predictors of WNV cases.
  • The developed models significantly outperformed random chance and a naïve persistence model in predicting county-level WNV risk.
  • Simulations indicated that eliminating drought and warm temperatures could reduce WNV cases by 45%.

Conclusions:

  • Climate-based early warning systems can effectively predict West Nile Virus (WNV) risk.
  • Specific climate patterns, including temperature extremes and precipitation anomalies, are key drivers of WNV outbreaks.
  • The modeling approach is adaptable for predicting other climate-sensitive annual outcomes, such as crop yield.