National Center for Supercomputing Applications, University of Illinois, Urbana-Champaign, Illinois, United States of America.
Department of Statistics, University of Illinois, Urbana-Champaign, Illinois, United States of America.
PLoS One. 2024 Jan 5;19(1):e0296283. doi: 10.1371/journal.pone.0296283. eCollection 2024.
West Nile virus (WNV), a flavivirus transmitted by mosquito bites, causes primarily mild symptoms but can also be fatal. Therefore, predicting and controlling the spread of West Nile virus is essential for public health in endemic areas. We hypothesized that socioeconomic factors may influence human risk from WNV. We analyzed a list of weather, land use, mosquito surveillance, and socioeconomic variables for predicting WNV cases in 1-km hexagonal grids across the Chicago metropolitan area. We used a two-stage lightGBM approach to perform the analysis and found that hexagons with incomes above and below the median are influenced by the same top characteristics. We found that weather factors and mosquito infection rates were the strongest common factors. Land use and socioeconomic variables had relatively small contributions in predicting WNV cases. The Light GBM handles unbalanced data sets well and provides meaningful predictions of the risk of epidemic disease outbreaks.
西尼罗河病毒（WNV）是一种通过蚊子叮咬传播的黄病毒，主要引起轻度症状，但也可能致命。因此，预测和控制西尼罗河病毒的传播对于流行地区的公共卫生至关重要。我们假设社会经济因素可能会影响人类感染 WNV 的风险。我们分析了一份有关天气、土地利用、蚊子监测和社会经济变量的清单，以预测芝加哥大都市区的 1 公里六角形网格中的 WNV 病例。我们使用两阶段的 LightGBM 方法进行分析，发现收入高于和低于中位数的六角形受到相同的顶级特征的影响。我们发现，天气因素和蚊子感染率是最强的共同因素。土地利用和社会经济变量在预测 WNV 病例方面的贡献相对较小。LightGBM 可以很好地处理不平衡数据集，并对传染病爆发的风险进行有意义的预测。
