RAPID: Adaptive Sampling Strategies for COVID-19 Mass Testing

RAPID：用于 COVID-19 大规模检测的自适应采样策略

• 批准号: 2032734
• 负责人: Xiaochen Xian
• 金额: $ 13.03万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2032734&HistoricalAwards=false
• 关键词: RAPID; Adaptive; Sampling; Strategies; COVID

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.

随着各州开始放松对物理距离的限制以重启经济活动，大规模COVID-19检测对于识别和遏制感染“热点”并避免更严重的疫情至关重要。 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，连同人口普查区一级的人口信息和流动模式，将沿着用于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是有限的，这个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LifeSouth血库和佛罗里达州卫生部合作，利用佛罗里达中北部的数据，预计将扩展到全国其他县、地区和州。 该研究方法预计将有助于减轻COVID-19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，以制定数据驱动的自适应抽样策略，根据（i）社区人口总数，（ii）每日测试能力和结果，（iii）与传染和发病率脆弱性有关的区块组人口统计数据（例如，种族、年龄结构、就业类型、住房拥挤情况），（iv）症状的普遍程度，以及（v）街区组人群的流动模式。 采样算法将从最近的测试结果（阳性和阴性）中“学习”，以随着时间的推移优化采样方法，平衡探索和开发，以避免忽视关键区域，同时确保经常对可疑区域进行采样。 该项目涉及四项任务：（i）通过与LifeSouth血库和佛罗里达卫生部合作收集COVID-19抗体检测和州检测数据;（ii）对社区医疗和社会脆弱性数据进行地理分析;（iii）开发自适应抽样算法，以确定社区各地区信息量最大的检测分配;（iv）通过与LifeSouth血库和佛罗里达州卫生部合作收集数据，以收集COVID-19抗体检测和州检测数据。（iv）进行成本效益分析，以确定日常预算，平衡测试能力和成本。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。