RAPID: Adaptive Sampling Strategies for COVID-19 Mass Testing

随着各州开始放宽距离限制，以重启经济活动，新冠肺炎的大规模检测将对识别和遏制感染“热点”并避免更严重的疫情爆发至关重要。这项快速反应研究(RAPID)赠款将支持收集有关血清学和病毒检测结果的时间敏感数据。这些数据，连同人口普查区级的人口信息和流动模式，将被用来制定一个数据驱动的大规模测试战略框架。由于测试资源在开始时将是有限的，该框架可以帮助指导测试策略最有效地使用这些资源。该项目涉及与南方生命血库和佛罗里达州卫生部的合作，以利用来自佛罗里达州中北部的数据，预计将可扩展到全国其他县、地区和州。这一研究方法有望帮助缓解新冠肺炎对公共卫生、社会和经济的负面影响。该项目将收集社区测试数据，以制定数据驱动的适应性抽样策略，以优化人口普查区组内的大规模测试，其基础是(I)社区人口总数，(Ii)日常测试能力和结果，(Iii)与传染病和发病率易感性相关的人口统计学数据(例如，种族、年龄结构、就业类型、住房拥挤)，(Iv)症状流行率，以及(V)人口普查组中人的流动模式。抽样算法将从最近的测试结果(包括阳性和阴性)中“学习”，随着时间的推移优化抽样方法，平衡勘探和开采，以避免忽视关键区域，同时确保经常对可疑区域进行抽样。该项目涉及四项任务：(I)通过与南方生命血库和佛罗里达州卫生部合作收集新冠肺炎抗体检测和州检测数据的数据；(Ii)社区医疗和社会脆弱性数据的地理分析；(Iii)开发自适应抽样算法，以确定社区中信息最丰富的测试分配；(Iv)成本效益分析，以确定每天的预算，以平衡检测能力和成本。该奖项反映了国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

As states begin to relax limitations on physical distancing in order to restart economic activity, mass testing for COVID-19 will be crucial in identifying and containing infection "hot spots" and to avoid more severe outbreaks. This Rapid Response Research (RAPID) grant will support the collection of time-sensitive data on serological and viral test outcomes. These data, along with census block-level demographic information and mobility patterns, will be used to develop a data-driven strategic framework for mass testing. As testing resources will be limited at the onset, this framework can help guide a testing strategy to use these resources most effectively. The project involves a collaboration with LifeSouth Blood bank and the State of Florida Department of Health to utilize data from north central Florida and is expected to be scalable to other counties, regions, and states across the Nation. This research approach is expected to help mitigate the negative impacts of COVID-19 on public health, society, and the economy.The project will collect community testing data in order to develop a data-driven adaptive-sampling strategy to optimize mass testing within census block groups based on (i) aggregated community population, (ii) daily testing capacity and outcomes, (iii) block group demographics related to contagion and morbidity vulnerability (e.g., race, age structure, employment type, housing crowding), (iv) symptom prevalence, and (v) mobility patterns of people in the block group. The sampling algorithms will “learn” from recent test outcomes (both positives and negatives) to optimize the sampling approach over time, balance exploration and exploitation to avoid overlooking critical areas while ensuring suspected areas are frequently sampled. The project involves four tasks: (i) data collection though collaboration with LifeSouth Blood bank and Florida Department of Health for COVID-19 antibody testing and state testing data; (ii) geographic analysis of community medical and social vulnerability data; (iii) development of an adaptive sampling algorithm to determine the most informative test allocation to regions in the community; (iv) cost-effective analysis to determine the daily budget to balance between testing power and costs.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.