McMillan Joseph R, Sun James, Chaves Luis Fernando, Armstrong Philip M
Department of Biological Sciences, Texas Tech University, 2901 Main St., Lubbock, TX 79409, USA.
Clark Scholars Program, Department of Biological Sciences, Texas Tech University, 2901 Main St., Lubbock, TX 79409, USA.
PNAS Nexus. 2025 Aug 19;4(8):pgaf227. doi: 10.1093/pnasnexus/pgaf227. eCollection 2025 Aug.
Predicting and projecting risk of West Nile virus (WNV) to humans in areas without mosquito surveillance data is a key limitation of many WNV surveillance programs. To better inform risk of WNV, we analyzed 20 years (2001-2020) of point-level mosquito surveillance data from Connecticut (CT), United States, using machine learning methods to determine the most informative weather variables and land cover classes associated with monthly collections and WNV detections in mosquitoes. All training models were assessed based on explained deviance, root mean square error, and parsimony of included variables then optimized using a backward selection process. We used these training models to create a predictive mapping framework that could spatially extrapolate the monthly risk of WNV activity in mosquitoes across the entirety of the Northeast United States (CT, Maine, Massachusetts, New Hampshire, New Jersey, New York, Rhode Island, and Vermont) at a 4 × 4 km resolution. We then validated WNV detection probabilities against observed human cases at the town level in CT and the county level for northeastern states using generalized linear (mixed effects) models. Our predicted town- and county-level WNV detection probabilities in mosquitoes were significantly associated with the odds of a human case occurring within the town and/or county. This methodology increases the utility of point-source mosquito surveillance data by creating a flexible workflow for predicting risk of WNV to humans across the Northeast United States using easily accessible online data sources.
在没有蚊虫监测数据的地区预测和推断西尼罗河病毒（WNV）对人类的风险是许多WNV监测项目的关键限制。为了更好地了解WNV风险，我们分析了美国康涅狄格州（CT）20年（2001 - 2020年）的逐点蚊虫监测数据，使用机器学习方法来确定与每月蚊虫采集量和WNV检测相关的最具信息性的气象变量和土地覆盖类别。所有训练模型均根据解释偏差、均方根误差和纳入变量的简约性进行评估，然后使用向后选择过程进行优化。我们使用这些训练模型创建了一个预测映射框架，该框架可以以4×4千米的分辨率在空间上推断美国东北部（CT、缅因州、马萨诸塞州、新罕布什尔州、新泽西州、纽约州、罗德岛州和佛蒙特州）整个地区蚊虫中WNV活动的每月风险。然后，我们使用广义线性（混合效应）模型，针对CT州城镇层面以及东北部各州县级的观察到的人类病例，验证了WNV检测概率。我们预测的城镇和县级蚊虫中WNV检测概率与城镇和/或县内发生人类病例的几率显著相关。这种方法通过创建一个灵活的工作流程，利用易于获取的在线数据源预测美国东北部WNV对人类的风险，提高了点源蚊虫监测数据的效用。
