Yi Chunlin, Cohnstaedt Lee W, Scoglio Caterina M
Department of Electrical and Computer Engineering, Carl R. Ice College of Engineering, Kansas State University, Manhattan, KS 66506, USA.
National Bio- and Agro-Defense Facility, Agricultural Research Service, United States Department of Agriculture, Manhattan, KS 66502, USA.
R Soc Open Sci. 2024 Dec 4;11(12):240513. doi: 10.1098/rsos.240513. eCollection 2024 Dec.
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.
西尼罗河病毒(WNV)是一种由蚊子传播的虫媒病毒,在美国仍然是一个持续存在的公共卫生挑战,其季节性疫情可能导致严重病例。在本研究中,我们详细介绍了一种针对西尼罗河病毒的实时预测系统,该系统采用了一种经过改进的 compartment 模型来考虑鸟类、蚊子和人类之间的传播动态,包括无症状病例以及天气因素的影响。利用数据同化技术,我们生成了2023年科罗拉多州西尼罗河病毒病例的每周预测,为公共卫生规划提供了有价值的见解。比较分析强调了将天气变量纳入我们的模型所实现的预测准确性的提高。