Ensemble forecast of human West Nile virus cases and mosquito infection rates
Ensemble forecast of human West Nile virus cases and mosquito infection rates人类西尼罗河病毒病例与蚊虫感染率的综合预报
West Nile virus (WNV) is now endemic in the continental United States; however, our ability to predict spillover transmission risk and human WNV cases remains limited. Here we develop a model depicting WNV transmission dynamics, which we optimize using a data assimilation method and two observed data streams, mosquito infection rates and reported human WNV cases. The coupled model-inference framework is then used to generate retrospective ensemble forecasts of historical WNV outbreaks in Long Island, New York for 2001 2014. Accurate forecasts of mosquito infection rates are generated before peak infection, and 465% of forecasts accurately predict seasonal total human WNV cases up to 9 weeks before the past reported case. This work provides the foundation for implementation of a statistically rigorous system for real-time forecast of seasonal outbreaks of WNV.
西尼罗河病毒(WNV)目前在美国大陆流行;然而，我们预测外溢传播风险和人类西尼罗河病毒病例的能力仍然有限。在这里，我们开发了一个描述西尼罗河病毒传播动态的模型，并利用数据同化方法和两个观测数据流、蚊子感染率和报告的西尼罗河病毒人间病例对该模型进行了优化。然后，使用耦合模型推理框架生成2001 - 2014年纽约长岛西尼罗河病毒历史暴发的回顾性整体预测。蚊子感染率的准确预测可在感染高峰出现之前进行，其中46.5%的预测可在过去报告病例的9周之前准确预测人类西尼罗河病毒的季节性总病例。这项工作为建立统计严密的西尼罗河病毒季节暴发实时预报系统提供了基础。
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