A Regionally Determined Climate-Informed West Nile Virus Forecast Technique

West Nile virus (WNV) infection has caused over 30,000 human cases of the severe, neuroinvasive form of the disease (West Nile virus Neuroinvasive Disease; WNND) and nearly 3,000 deaths in the U.S. Despite known links to observable climate factors, no effective nationwide WNV or WNND forecast exists. We aimed to produce a skillful, nationwide WNND forecast built upon regionally varying relationships between climate factors and WNND. After examining the relationships between climate conditions and annual WNND caseload for 11 regions in the U.S., we incorporated the most salient climate factors—most commonly drought and temperature—into a regionally determined nationwide WNND statistical forecast model using a Bayesian regression framework. We retrospectively generated forecasts from 2005 to 2022 and compared forecast skill against various benchmarks, including a simple, historical case-driven model. Our regional, climate-informed WNND retrospective forecasts outperformed a benchmark model only informed by historical WNND case data across all regions, as well as in a nationally aggregated score (univariable: 18.8% [4.7%–27.7%], bivariable: 21.8% [7.0%–30.7%] improvement). The regional forecasts also outperformed an ensemble model generated from a recent WNV forecasting competition and a parallel, county-level, regional climate-informed forecast outperformed forecasts from the same competition. Importantly, our approach to WNND forecast development aggregated county-level data to broader regions to boost statistical signal and capture the regionally varying influences of climate conditions on annual WNND caseload. The advances here represent a potential path toward actionable broad-scale WNV forecasts.