RAPID: Identifying Geographic and Demographic Drivers of Rural Disease Transmission for Improved Modeling and Decision Making

这项快速反应研究（RAPID）赠款将提高对农村地区疾病传播驱动因素的了解，为改善农村社区的决策和公共卫生管理提供见解。目前与COVID-19传播建模相关的大多数研究都集中在城市系统上。鉴于农村和城市系统在人口、社会流动性、交通和建筑环境特征方面存在巨大差异，预计农村的传播模式与城市不同。本项目将审查情况是否如此;查明造成任何差异的关键因素;以及应如何调整模式以更好地适应农村条件。根据这些发现，该小组将建立一个非常适合农村社区的流行病学模型。该项目还将开始评估如何调整风险预防政策和信息，以便在农村社区发挥最大效力。该研究团队将与县级和州级公共卫生管理人员分享相关研究结果，并帮助他们将研究结果纳入最佳实践。该研究的目标是进行空间、地理、公共卫生、废水基础设施和社会数据收集和综合的知情过程，以改善农村社区的流行病管理。项目组将考察北卡罗来纳州的3个农村县和3个城市县。初步数据将从州和县一级的COVID-19流行病学数据以及COVID-19研究的其他可用的已发表信息中收集和合成，作为可验证的暴露-感染-恢复（SEIR）建模基础方法的有价值输入。 在此基础上，将采用一个有指导的数据收集和综合过程，确定农村和城市疾病传播的重要因素的优先次序。可用的数据来源包括健康监测、基于手机的移动性、土地使用特征、通勤模式、基本商业邻近度、公共卫生基础设施和医疗服务可用性。该小组将同时在选定的县的选定污水和包装处理系统中收集废水样本，以量化SARS-CoV-2在相关地点的流行程度。 这将为疾病患病率提供额外的非临床指标。鉴于目前临床检测数据的不准确性，特别是在农村地区，这些疾病存在数据将构成疾病存在的关键衡量标准，以验证SEIR模型中出现的见解，并评估公共卫生模型中使用的其他指标。该项目的最后阶段将是纠正疾病传播驱动因素的概念框架，以及使用NC监测系统数据的初始SEIR/空间建模方法，并与县和州一级的公共卫生管理人员和流行病学工作，将研究结果纳入最佳实践。该奖项反映了NSF的法定使命，并已被认为是值得支持的，通过评估使用基金会的智力价值，更广泛的影响审查标准。

This Rapid Response Research (RAPID) grant will improve understanding of drivers of disease transmission in rural areas, providing insights for improved decision-making and public health management in rural communities. The majority of current research relevant to modeling COVID-19 spread is focused on urban systems. Given the vast differences in demographic, social mobility, transportation, and built environment characteristics between rural and urban systems, it is expected that rural spread patterns are different from urban. This project will examine whether this is the case; identify key factors that account for any differences; and how models should be adjusted to better fit rural conditions. Based on those findings, the team will build an epidemiological model well suited to rural communities. The project will also begin the process of evaluating how well risk prevention policies and messages could be adjusted for maximum effectiveness in rural communities. The research team will share relevant findings with county- and state-level public health managers and help them incorporate the findings into best practices.The goal of this research is to conduct an informed process of spatial, geographic, public health, wastewater infrastructure and social data collection and synthesis for improved pandemic management in rural communities. The project team will examine 3 rural and 3 urban counties in North Carolina. Initial data will be collected and synthesized from COVID-19 epidemiological data at the state and county level, as well as other available published information from COVID-19 research as valuable input to a susceptible-exposed-infected-recovered (SEIR) modeling base approach. From there, a guided process of data collection and synthesis will be used to prioritize factors of importance in disease transmission across rural and urban. Available data sources include health surveillance, cell-phone based mobility, land use features, commuting patterns, essential business proximity, public health infrastructure, and medical care availability. The team will simultaneously gather wastewater samples in selected sewage and package treatment systems across selected counties to quantify the prevalence of SARS-CoV-2 in the relevant locations. This will provide an additional non-clinical metric for disease prevalence. Given current inaccuracies of clinical testing data, particularly in rural areas, these disease-presence data will constitute a key measure of disease presence against which to validate insights emerging from the SEIR model as well as to assess other metrics being used in public health models. The final stages of the project will be to rectify the conceptual framework of disease transmission drivers, and initial SEIR/spatial modeling approaches with NC surveillance system data, and work with county and state level public health managers and epidemiological to incorporate the findings into best practices.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.