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February 2, 2026GeoHealthOpen Access

A Regionally Determined Climate‐Informed West Nile Virus Forecast Technique

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Authors

RHRyan D. HarpCenters for Disease Control and PreventionKHKaren M. HolcombCenters for Disease Control and PreventionSBStanley G. BenjaminNational Oceanic and Atmospheric Administration

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Implication

Forecasting technique predicts West Nile virus neuroinvasive disease using regional climate data, implying enhanced accuracy.

Key Points

  • To create a nationwide forecast model for West Nile virus neuroinvasive disease based on regional climate relationships.
  • Examined climate conditions and their relationship with WNND caseload in 11 U.S. regions.
  • Developed a statistical forecast model using Bayesian regression.
  • Retrospectively generated forecasts from 2005 to 2022.
  • Compared forecast skill against benchmarks, including historical case-driven models.
  • Regional climate-informed forecasts outperformed historical case-driven models across all regions.
  • Improvement in forecast skill was noted with univariable models (18.8%) and bivariable models (21.8%).
  • The approach utilized aggregated county-level data to enhance statistical signal and capture regional influences.

Cite This Study

Harp et al. (2026) studied this question.

synapsesocial.com/papers/6980feb9c1c9540dea8111a6https://doi.org/10.1029/2025gh001657
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