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，评估新冠肺炎疫情期间美国的药物和非药物干预策略。在当前这场史无前例的危机中，当社会对新冠肺炎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定。研究评估干预策略可以帮助决策者确定遏制和缓解新冠肺炎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，提供对美国各州和县级各种遏制和缓解措施的医疗需求和有效性的估计。一个开放访问的互动仪表板将提供重要的见解，政策制定者可以利用这些见解在大流行期间根据可用资源以及在各种情景和地点下做出和调整明智的决策。将使用随机建模框架。该框架将遵循疾病的自然历史，对易感-暴露-感染-移除(SEIR)进行划分，并按16个年龄组进行分层。该模型将使用代表美国各州和县的接触模式、人口统计变量、住院、死亡、发病率、疾病特征和关键医疗基础设施(例如医院床位)容量的数据进行参数化。为了确定针对新冠肺炎爆发阶段量身定做的诊断测试的最佳部署策略，研究人员将模拟新冠肺炎的传播动力学，以预测疾病的相关后果。战略将考虑到可用的检测工具包数量。由于新冠肺炎的症状很难与流感区分开来，因此将根据历史数据趋势调整流感样疾病的就医病例数量。研究人员将探索一系列策略，从对出现新冠肺炎症状的医院的个人进行检测，到年龄和风险依赖监测，来自高危地区的个人将接受检测。为了评估各种非药物干预措施对新冠肺炎传播和相应的医疗保健激增能力的影响，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。这一奖项反映了美国国家科学基金会的法定使命，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。

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.