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

ATD：合作研究：利用时空天气模式预测媒介传播疾病的威胁

• 批准号: 1830312
• 负责人: Bo Li
• 金额: $ 21.28万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1830312&HistoricalAwards=false
• 关键词: ATD; Collaborative; Research; Predicting; Threat

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.

病媒传播的疾病几乎影响到地球上的每一个人。 蚊子是分布最广的病媒，严重威胁人类的生命和健康。西尼罗河病毒（WNV）是蚊子传播的疾病之一，目前仍没有有效的治疗方法;迄今为止，美国疾病控制和预防中心已报告了超过40，000例病例。温度和降水是影响蚊子数量的两个最重要的天气变量，从而影响西尼罗河病毒的传播周期。 蚊子感染率（MIR）被认为是研究西尼罗河病毒风险的重要媒介。基于伊利诺伊州西尼罗河病毒的监测数据，该项目旨在开发新的方法和算法，利用天气和环境变量研究西尼罗河病毒和MIR。具体来说，研究人员计划首先对MIR进行预测，然后描述温度和降水的空间模式，以确定WNV人类疾病和MIR的风险水平。他们还将建立一个西尼罗河病毒指数，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对未来的当前风险。这项研究将促进基本统计方法的发展以及统计和公共卫生之间的合作。研究生和本科生将从事科学研究方面的工作。该项目将提供关于气候变化对国家安全影响的新成果，这对广大公众和政策制定者具有普遍意义和重要性。该项目的方法包括一个空间变化系数模型，其中包含功能性天气协变量，用于预测MIR，以及多重-一种测试方法来表征温度和降水的空间模式，以最终将天气模式分类为不同的风险与WNV相比。从历史数据中学习到的统计模型和算法将应用于缩小的未来天气数据，以研究气候变化对WNV人类疾病和MIR的影响。分析将基于大量数据，包括WNV人类病例，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本西尼罗河病毒研究有效，而且可以作为广泛的病媒传播疾病的通用方法。函数协变量的空间变化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空间域上变化。西尼罗河灾害的空间天气型特征和指数的建立为研究和防范西尼罗河灾害提供了新的视角。与以往的方法，评估两个时空随机场之间的差异作为一个整体，多重测试方法在这个项目中可以准确地检测差异发生。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政策制定者至关重要;该项目的结果预计将为此提供可靠的资源。该奖项反映了NSF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。

项目成果

The Impact of Adulticide on Culex Abundance and Infection Rate in North Shore of Cook County, Illinois

杀成虫对伊利诺伊州库克县北岸库蚊数量和感染率的影响

• DOI: 10.2987/21-7036
• 发表时间: 2022
• 期刊: Journal of the American Mosquito Control Association
• 影响因子: 1
• 作者: Sass, Danielle;Li, Bo;Clifton, Mark;Harbison, Justin;Xamplas, Christopher;Smith, Rebecca
• 通讯作者: Smith, Rebecca

Evaluating Proxy Influence in Assimilated Paleoclimate Reconstructions—Testing the Exchangeability of Two Ensembles of Spatial Processes

评估同化古气候重建中的代理影响——测试两个空间过程系综的可交换性

• DOI: 10.1080/01621459.2020.1799810
• 发表时间: 2020
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7
• 作者: Harris, T.

Scalable multiple changepoint detection for functional data sequences

• DOI: 10.1002/env.2710
• 发表时间: 2020-08
• 期刊: Environmetrics
• 影响因子: 1.7
• 作者: Trevor Harris;Bo Li;J. D. Tucker

Elastic Depths for Detecting Shape Anomalies in Functional Data

• DOI: 10.1080/00401706.2020.1811156
• 发表时间: 2019-07
• 期刊: Technometrics
• 影响因子: 2.5
• 作者: Trevor Harris;J. Tucker;Bo Li;L. Shand

Detection of Local Differences in Spatial Characteristics Between Two Spatiotemporal Random Fields

两个时空随机场之间空间特征的局部差异检测

• DOI: 10.1080/01621459.2020.1775613
• 发表时间: 2021
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7
• 作者: Yun, Sooin;Zhang, Xianyang;Li, Bo
• 通讯作者: Li, Bo