A real-time forecasting and estimating system of West Nile virus: a case study of the 2023 WNV outbreak in Colorado, USA

Abstract

West Nile virus (WNV) is a mosquito-borne arbovirus that remains a persistent public health challenge in the USA, with seasonal outbreaks that can lead to severe cases. In this study, we detail a real-time prediction system for WNV that incorporates an adapted compartment model to account for the transmission dynamics among birds, mosquitoes and humans, including asymptomatic cases and the influence of weather factors. Using data assimilation techniques, we generate weekly WNV case forecasts for Colorado in 2023, providing valuable insights for public health planning. Comparative analyses underscore the enhanced forecast accuracy achieved by integrating weather variables into our models.

Keywords

• West Nile virus
• ensemble Kalman filter
• prediction
• compartmental models
• early warning systems
• public health preparedness