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

RAPID：遏制美国的 COVID-19 疫情

• 批准号: 2027755
• 负责人: Alison Galvani
• 金额: $ 20万
• 依托单位: Yale University
• 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: 2020-04-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027755&HistoricalAwards=false
• 关键词: RAPID; Curbing; COVID; 19; outbreak

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.

该项目将产生实用的数学建模框架，该框架将在美国大流行期间评估美国的药物和非药物干预策略。在当前史无前例的危机中，必须迅速做出公共卫生决定，当社区中Covid-19的负担存在很大的不确定性时。评估干预策略的研究可以帮助决策者确定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，该框架将估计美国州和县一级的各种遏制和缓解方法的医疗保健需求和有效性，一个开放式，交互式仪表板将提供政策制定者可以提供的重要见解。根据可用资源以及各种场景和地区，用于大流行期间，用于在大流行过程中做出和调整明智的决策。将使用随机建模框架。该框架将遵循该疾病的自然史，并通过易感暴露感染的回火（SEIR）分隔，并由16个年龄组进行分层。该模型将用于参数化的数据，以代表接触模式，人口统计学变量，住院，死亡，发病率，疾病特征以及美国州和县关键医疗保健基础设施（例如医院床）的能力。为了确定针对COVID-19爆发阶段量身定制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，以预测疾病相关结果。策略将说明可用的测试套件数量。由于Covid-19的症状很难与流感区分开，因此将使用历史数据趋势来调整寻求医疗治疗的类流感样的疾病病例的数量。研究人员将探索从与199个症状的医院的个人测试到年龄和风险依赖性监视的策略，这些策略将在其中测试高风险地区的个人。为了评估各种非药物干预措施对COVID-19传播的影响和随附的医疗保健激增能力，研究人员将探讨不同公共卫生措施的后果，例如接触跟踪，自我验证和自我隔离。 NSF的法定使命，并使用基金会的知识分子优点和更广泛的影响审查标准来评估值得支持。

项目成果

Can the USA return to pre-COVID-19 normal by July 4?

• DOI: 10.1016/s1473-3099(21)00324-8
• 发表时间: 2021-08
• 期刊: The Lancet. Infectious diseases
• 作者: Moghadas SM;Sah P;Vilches TN;Galvani AP
• 通讯作者: Galvani AP

Accelerated vaccine rollout is imperative to mitigate highly transmissible COVID-19 variants.

• DOI: 10.1016/j.eclinm.2021.100865
• 发表时间: 2021-05
• 期刊: EClinicalMedicine
• 影响因子: 15.1
• 作者: Sah P;Vilches TN;Moghadas SM;Fitzpatrick MC;Singer BH;Hotez PJ;Galvani AP
• 通讯作者: Galvani AP

Multifaceted strategies for the control of COVID-19 outbreaks in long-term care facilities in Ontario, Canada

加拿大安大略省长期护理机构中控制 COVID-19 疫情的多方面策略

• DOI: 10.1016/j.ypmed.2021
• 发表时间: 2021
• 期刊: Preventive medicine
• 影响因子: 5.1
• 作者: Vilches TN, Nourbakhsh S

Routine saliva testing for the identification of silent coronavirus disease 2019 (COVID-19) in healthcare workers.

• DOI: 10.1017/ice.2020.1413
• 发表时间: 2021-10
• 期刊: Infection control and hospital epidemiology
• 影响因子: 4.5
• 作者: Zhang K;Shoukat A;Crystal W;Langley JM;Galvani AP;Moghadas SM
• 通讯作者: Moghadas SM

Racial disparities in COVID-19 mortality across Michigan, United States.

• DOI: 10.1016/j.eclinm.2021.100761
• 发表时间: 2021-03
• 期刊: EClinicalMedicine
• 影响因子: 15.1
• 作者: Parpia AS;Martinez I;El-Sayed AM;Wells CR;Myers L;Duncan J;Collins J;Fitzpatrick MC;Galvani AP;Pandey A
• 通讯作者: Pandey A