ATD: Collaborative Research: Predicting the Threat of Vector-Borne Illnesses Using Spatiotemporal Weather Patterns

病媒传播的疾病几乎影响到地球上的每个人。蚊子是分布最广泛的疾病媒介，对人类生命和健康构成严重威胁。西尼罗病毒（WNV）是目前尚无有效治疗方法的蚊媒疾病之一；到目前为止，美国疾病控制和预防中心已经报告了4万多例病例。气温和降水是影响蚊子种群数量从而影响西尼罗河病毒传播周期的两个最重要的天气变量。蚊虫感染率（MIR）被认为是研究西尼罗河病毒危险性的重要媒介。该项目基于伊利诺斯州西尼罗河病毒的监测数据，旨在开发利用天气和环境变量研究西尼罗河病毒和MIR的新方法和算法。具体而言，研究人员计划首先对MIR进行预测，然后表征温度和降水的空间格局，以确定西尼罗河病毒人类疾病和MIR的风险水平。他们还将建立西尼罗河病毒指数，为媒介传播的疾病风险提供可靠和可解释的警告。最后，由于蚊子传播的疾病特别受到气温上升、降水模式变化和极端天气事件频率增加的影响，该项目旨在定量和定性地预测气候变化下当前对未来的风险。这项研究将促进基本统计方法的发展以及统计与公共卫生之间的合作。研究生和本科生将从事科学研究的各个方面。该项目将提供有关气候变化对国家安全影响的新结果，这对广大公众和决策者具有普遍利益和重要性。本项目的方法包括一个具有功能天气协变量的空间变系数模型来预测MIR，以及一个多重检验方法来表征温度和降水的空间格局，从而最终将天气模式划分为与西尼罗河病毒有关的不同风险水平。从历史数据中学习到的统计模型和算法将应用于缩小规模的未来天气数据，以研究气候变化对西尼罗河病毒人类疾病和MIR的影响。分析将基于大量数据，包括西尼罗河病毒病例、MIR、当前和未来的时空随机天气过程、土地覆盖和日光长度。该项目中使用的统计方法不仅对西尼罗河病毒研究有效，而且可以作为广泛的媒介传播疾病的一般方法。带函数协变量的空间变系数模型考虑了回顾性天气对MIR的连续和动态影响，同时允许MIR与天气和其他环境变量之间的关系在一个空间域内变化。空间天气模式特征和西尼罗河病毒指数的建立为研究和预防西尼罗河病毒风险提供了新的视角。与以往评估两个时空随机场之间整体差异的方法相比，本项目中的多重测试方法可以准确地检测出差异发生的位置。这一特征对于区域风险检测至关重要。量化气候变化对病媒传播疾病的影响对决策者至关重要；预期该项目的成果将为此目的提供可靠的资源。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Vector-borne diseases affect virtually everyone on earth. Mosquitoes are the most widely distributed disease vectors and are a serious threat to human life and health. West Nile virus (WNV) is one of the mosquito-borne diseases for which there is still no effective treatment; to date, the Centers for Disease Control and Prevention has reported over 40,000 cases across the United States. Temperature and precipitation are the two most important weather variables that affect mosquito populations and thus affect the WNV transmission cycle. The mosquito infection rate (MIR) is considered an important mediator to study WNV risk. Based on surveillance data for WNV in Illinois, this project aims to develop new methodologies and algorithms to study WNV and MIR using weather and environmental variables. Specifically, the investigators plan first to make predictions of MIR and then characterize the spatial pattern of temperature and precipitation to identify the risk level of WNV human illness and MIR. They will also establish a WNV Index to provide a reliable and interpretable warning for vector-borne disease risk. Finally, since mosquito-borne diseases are particularly affected by rising temperatures, changing precipitation patterns, and a higher frequency of extreme weather events, the project aims to both quantitatively and qualitatively project the current risk to the future under climate change. The research will foster fundamental statistical methodology development as well as collaborations between statistics and public health. Graduate and undergraduate students will be engaged in aspects of the scientific research. The project will provide new results on the impact of climate change on national security, of general interest and importance to the wider public and policymakers.The methods of this project include a spatially-varying-coefficient model with functional weather covariates to make predictions of MIR, as well as a multiple-testing approach to characterize the spatial pattern of temperature and precipitation for ultimately classifying the weather pattern into different risk levels with respect to WNV. The statistical models and algorithms learned from the historical data will be applied to downscaled future weather data to study the impact of climate change on WNV human illness and MIR. The analyses will be based on massive data including WNV human cases, MIR, current and future spatio-temporal stochastic weather processes, land cover, and the length of daylight. The statistical methods used in the project are not only effective for this WNV study but can be a general methodology for a wide range of vector-borne diseases. The spatially-varying-coefficient model with functional covariates takes the continuous and dynamic influence of the retrospective weather on MIR into account while allowing the relationship between MIR and weather and other environmental variables to vary over a spatial domain. The characterization of the spatial weather pattern and the establishment of WNV Index provide a new perspective to study and prevent WNV risk. Compared to previous methods that evaluate the difference between two spatio-temporal random fields as a whole, the multiple-testing approach in this project can detect exactly where the differences occur. This feature is crucial for regional risk detection. Quantifying the impact of climate change on vector-borne diseases is essential to policymakers; the results of the project are expected to provide a reliable resource for such purposes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.