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

被引：0

作者：

Yi, Chunlin ^{[1
]}

Cohnstaedt, Lee W. ^{[2
]}

Scoglio, Caterina M. ^{[1
]}

机构：

[1] Kansas State Univ, Carl R Ice Coll Engn, Dept Elect & Comp Engn, Manhattan, KS 66506 USA

[2] United States Dept Agr, Agr Res Serv, Natl Bioand Agrodef Facil, Manhattan, KS 66502 USA

来源：

ROYAL SOCIETY OPEN SCIENCE | 2024年 / 11卷 / 12期

基金：

美国农业部; 美国食品与农业研究所;

关键词：

West Nile virus; ensemble Kalman filter; prediction; compartmental models; early warning systems; public health preparedness; PATHOGENS; MODEL;

D O I：

10.1098/rsos.240513

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页数：12

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