RAPID: Curbing the COVID-19 outbreak in the United States

该项目将产生一个实用的数学建模框架，用于评估美国在COVID-19大流行期间的药物和非药物干预策略。在当前这场前所未有的危机中，当社区对COVID-19的负担存在很大程度的不确定性时，必须迅速做出公共卫生决策。评估干预策略的研究可以帮助决策者确定遏制和缓解COVID-19所需的适当控制措施和强度。为了实现这些目标，研究人员将开发一个数学框架，该框架将提供美国州和县一级的医疗保健需求和各种遏制和缓解方法的有效性的估计。交互式仪表板将提供重要的见解，决策者可以利用这些见解在流行病期间根据可用资源以及各种将使用随机建模框架。该框架将遵循疾病的自然史，划分为易感-暴露-感染-清除（SEIR），并按16个年龄组分层。该模型将使用代表接触模式、人口统计学变量、住院、死亡、发病率、疾病特征和关键医疗保健基础设施（例如，医院病床）在美国各州和县。为了确定针对COVID-19爆发阶段的诊断测试的最佳部署策略，研究人员将模拟COVID-19的传播动态，以预测疾病的相关结果。战略将考虑到检测试剂盒的可用数量。由于COVID-19的症状难以与流感区分，因此将使用历史数据趋势来调整流感样疾病求医的病例数。研究人员将探索各种策略，从对在医院出现COVID-19症状的个人进行测试，到对来自高风险地区的个人进行测试的年龄和风险依赖性监测。为了评估各种非药物干预措施对COVID-19传播的影响以及随之而来的医疗保健激增能力，研究人员将探索不同公共卫生措施的后果，如接触者追踪，自我隔离和自我隔离。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。

This project will produce a practical mathematical modeling framework that will assess pharmaceutical and non-pharmaceutical intervention strategies in the US, over the course of the COVID-19 pandemic. In the midst of this current unprecedented crisis, public health decisions must be made quickly when there is a large level of uncertainty about the burden of COVID-19 in the community. Research to evaluate intervention strategies can help decision-makers to identify the proper control measures and intensity needed for containment and mitigation of COVID-19. To accomplish these goals, the researchers will develop a mathematical framework that will provide estimates of healthcare demands and effectiveness of various containment and mitigation approaches at state and county levels in the U.S. An open-access, interactive dashboard will provide vital insights that policy makers can use to make and adjust informed decisions during the course of the pandemic depending on available resources as well as under various scenarios and locales.A stochastic modelling framework will be used. The framework will follow the natural history of the disease, with a compartmentalization of susceptible-exposed-infected-removed (SEIR) and stratified by 16 age groups. The model will be parametrized useing data representing contact patterns, demographic variables, hospitalizations, deaths, incidence, disease characteristics, and capacity of critical healthcare infrastructure (e.g., hospital beds) in US states and counties. To determine an optimal deployment strategy of diagnostic tests tailored to the stage of the COVID-19 outbreak, the researchers will simulate transmission dynamics of COVID-19 to project relevant outcomes of disease. Strategies will account for the available number of testing kits. As the symptoms of COVID-19 are difficult to distinguish from influenza, historical data trends will be used to adjust the number of cases of influenza-like-illness seeking medical treatment. The researchers will explore strategies ranging from testing of individuals presenting in hospitals with COVID-19 symptoms to age and risk dependent surveillance where individuals from high-risk areas would be tested. To assess the impact of various non-pharmaceutical interventions on COVID-19 transmission and the accompanying healthcare surge capacity, the researchers will explore the consequences of different public health measures such as contact tracing, self-quarantine, and self-isolation.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.