Skip to main content
ARS Home » Research » Publications at this Location » Publication #417503
Research Project: Japanese Encephalitis Virus Prevention and Mitigation Strategies

Location: Location not imported yet.

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

Author
item YI, CHUN LIN - Kansas State University
item Cohnstaedt, Lee
item SCOGLIO, CATERINA - Kansas State University

Submitted to: Royal Society Open Science
Publication Type: Literature Review
Publication Acceptance Date: 10/25/2024
Publication Date: 12/4/2024
Citation: Yi, C., Cohnstaedt, L.W., Scoglio, C. 2024. A real-time forecasting and estimating system of West Nile virus: a case study of the 2023 WNV outbreak in Colorado, USA. Royal Society Open Science. 11(12). Article 240513. https://doi.org/10.1098/rsos.240513.
DOI: https://doi.org/10.1098/rsos.240513

Interpretive Summary: West Nile virus (WNV) is a mosquito-transmitted virus that is a considerable public health challenge in the United States because seasonal outbreaks of mosquitoes may lead to many viral outbreaks in humans. In this study, a real-time case forecasting system for West Nile Virus that incorporates the virus transmission dynamics among birds, mosquitoes, and humans, including when no clinical symptoms are present. Additionally, the model accounts for the influence of weather factors such as rainfall and average daily temperature. The model was used to generate weekly WNV case forecasts for Colorado in 2023. The forecasts can provide valuable insights for public health planning such as the positioning of resources and identifying where to treat for mosquitoes prior to an outbreak. Enhanced forecast accuracy was achieved by integrating weather variables into the multispecies model.

Technical Abstract: West Nile virus 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 West Nile Virus (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.