AI Insight
Researchers developed four mathematical models of increasing complexity to forecast West Nile virus infection in mosquitoes and humans in Maricopa County, Arizona, using 15 years of weekly surveillance data. While all model configurations fit historical data equally well, models incorporating both bird population dynamics and weather variables produced more accurate short-term forecasts of mosquito abundance and infection rates compared to simpler models. All model types successfully predicted human case counts better than baseline methods, with forecast accuracy peaking during summer and fall months.
Why it matters
This research demonstrates that integrating weather and bird dynamics into disease forecasting models improves prediction accuracy for mosquito-borne illness, providing public health officials with better tools for allocating surveillance and prevention resources. As climate change continues to alter disease transmission patterns, these scalable forecasting methods could enhance preparedness for West Nile virus outbreaks in high-burden regions.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Mosquito-borne diseases pose a growing public health challenge as climate change reshapes vector population dynamics. West Nile virus (WNV), transmitted between birds and Culex mosquitoes, disproportionately affects Maricopa County, Arizona, one of the nation’s highest-burden counties, yet whether models that include weather and avian dynamics improve forecast accuracy remains unclear. Using a 15-year weekly time series of mosquito abundance, mosquito infection prevalence, and human cases, we developed four mechanistic model configurations of varying complexity, from mosquito-human dynamics alone to full models incorporating avian dynamics and weather forcing. We fitted each model to the weekly-observed data, generated probabilistic 1- and 2-week-ahead forecast horizons, and evaluated forecasts against a historical baseline. All configurations fit the data equally regardless of weather or avian dynamics. However, models incorporating both birds and weather created more accurate forecasts of mosquito abundance and mosquito infection prevalence, and all configurations outperformed the baseline for forecasting human cases. Forecast accuracy was highest in summer and fall, and ensemble aggregation sometimes outperformed every individual model, stabilizing predictions across the 15-year record. These findings indicate that avian and weather dynamics are most critical for predicting mosquito-specific data, positioning this framework as a scalable tool for public health planning for WNV surveillance under climate change.