Evaluating the roles of weather and bird dynamics in accurately forecasting West Nile virus infection in mosquitoes and humans
- 1Northern Arizona University
Abstract
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 nations 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.
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BibTeX
@misc{oshinubi2026evaluating,
title = {{Evaluating the roles of weather and bird dynamics in accurately forecasting West Nile virus infection in mosquitoes and humans}},
author = {K. Oshinubi and J. Covington and N. Busser and J. Townsend and J. Will and I. Ruberto and M. Kretschmer and Y. Chen and E. Doerry and C. M. Hepp and J. R. Mihaljevic},
howpublished = {medRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.27.26361564},
url = {https://www.medrxiv.org/content/10.64898/2026.08.27.26361564v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Evaluating the roles of weather and bird dynamics in accurately forecasting West Nile virus infection in mosquitoes and humans
AU - K. Oshinubi
AU - J. Covington
AU - N. Busser
AU - J. Townsend
AU - J. Will
AU - I. Ruberto
AU - M. Kretschmer
AU - Y. Chen
AU - E. Doerry
AU - C. M. Hepp
AU - J. R. Mihaljevic
PY - 2026
DA - 2026/08/27
PB - medRxiv
DO - 10.64898/2026.08.27.26361564
UR - https://www.medrxiv.org/content/10.64898/2026.08.27.26361564v1
AB - 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 nations 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.
ER -