RAPID: Mathematical Models for Understanding Key Epidemiological Parameters and Transmission of SARS-CoV-2

RAPID：了解 SARS-CoV-2 关键流行病学参数和传播的数学模型

• 批准号: 2031756
• 负责人: Alan Perelson
• 金额: $ 20万
• 依托单位: New Mexico Consortium
• 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 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031756&HistoricalAwards=false
• 关键词: RAPID; Mathematical; Models; Understanding; Key

The COVID-19 pandemic, caused by SARS-CoV-2, represents a true global emergency and crisis that needs to be addressed immediately. Currently we do not have a good understanding of quantitative estimates of key epidemiological parameters, such as the time from infection to becoming infectious, the duration of infectiousness, and how these durations are affected by symptom severity. We do not know how much transmission is driven by asymptomatic individuals and individuals with mild, moderate, or severe symptoms. Lastly, we do not know how the viral load relates to the infectiousness of an individual. By constructing multiscale models and integrating multiple streams of data from different biological scales into a coherent model framework, the project will address these unknowns through mathematical modeling and thus advance our understanding of COVID-19 transmission. This understanding will be then used to make precise predictions and evaluations of the impacts of interventions, i.e. much needed intellectual advancement to address the current COVID-19 global pandemic. The results of the project will be presented to public health professionals and government officials to aid decision making through regular meetings and connections that the team members participate and maintain. For example, co-PI Ke regularly participates in weekly CDC modeling group meetings and in monthly meetings with a working group commissioned by the White House called PPFST. Co-PIs Hengartner and Romero-Severnson have close connections with the New Mexico Department of Health. The PIs will ensure that the project is designed and formulated to address critical public health questions and that the results can be used by public health officials.The objective of this research project is to advance the fundamental understanding of key epidemiological parameters and determinants for SARS-CoV-2 that are essential to better quantify and predict SARS-CoV-2 transmission dynamics. The investigators will use mathematical modeling of SARS-CoV-2 dynamics across multiple scales. First, the PI will develop within-host models of SARS-CoV-2 infection and will fit the model to data from literature to estimate parameters such as the time from cell infection to release of virus into bodily fluids, the rate of viral production from infected cells, the infected cell lifespan, and other factors that link to transmission and disease severity. Another aim is to use the model to predict the effectiveness of drug therapy as a function of person's viral load. Second, the PI will tie the within-host dynamics to clinical factors, such as the time between infection and viral shedding, time to symptoms, and in some cases to time of death, which are important in understanding transmission dynamics and intervention effectiveness. Ultimately, this project will provide a quantitative framework to evaluate the effectiveness of pharmaceutical and non-pharmaceutical interventions, e.g., quarantine, school closures, and other means of social distancing.This grant is being awarded using funds made available by the Coronavirus Aid, Relief, and Economic Security (CARES) Act supplement allocated to MPS.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.

由SARS-CoV-2引起的COVID-19大流行是一个真正的全球紧急情况和危机，需要立即加以解决。目前，我们对关键流行病学参数的定量估计没有很好的了解，例如从感染到具有传染性的时间，传染性的持续时间以及这些持续时间如何受到症状严重程度的影响。我们不知道有多少传播是由无症状的个体和具有轻度、中度或重度症状的个体驱动的。最后，我们不知道病毒载量如何与个体的传染性相关。通过构建多尺度模型，并将来自不同生物尺度的多个数据流整合到一个连贯的模型框架中，该项目将通过数学建模来解决这些未知因素，从而推进我们对COVID-19传播的理解。然后，这种理解将用于对干预措施的影响进行精确预测和评估，即应对当前COVID-19全球大流行急需的知识进步。该项目的结果将提交给公共卫生专业人员和政府官员，以通过团队成员参与和保持的定期会议和联系来帮助决策。例如，共同PI柯定期参加每周的CDC建模小组会议，并每月与白宫委托的一个名为PPFST的工作组举行会议。亨加特纳和罗梅罗·塞文森与新墨西哥州卫生部有着密切的联系。项目负责人将确保该项目的设计和制定能够解决关键的公共卫生问题，并确保公共卫生官员能够使用研究结果。该研究项目的目标是促进对SARS-CoV-2的关键流行病学参数和决定因素的基本理解，这些参数和决定因素对于更好地量化和预测SARS-CoV-2传播动态至关重要。研究人员将在多个尺度上使用SARS-CoV-2动力学的数学模型。首先，PI将开发SARS-CoV-2感染的宿主内模型，并将该模型与文献数据拟合，以估计参数，如从细胞感染到病毒释放到体液中的时间，受感染细胞的病毒产生速率，受感染细胞的寿命以及与传播和疾病严重程度相关的其他因素。另一个目的是使用该模型来预测药物治疗的有效性作为一个人的病毒载量的函数。其次，PI将宿主内动态与临床因素联系起来，例如感染和病毒脱落之间的时间，出现症状的时间，以及在某些情况下死亡的时间，这对于理解传播动态和干预有效性非常重要。最终，该项目将提供一个量化框架，以评估药物和非药物干预措施的有效性，例如，隔离，学校关闭和其他社交距离的手段。该补助金是使用分配给MPS的冠状病毒援助，救济和经济安全（CARES）法案补充提供的资金颁发的。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。

项目成果

Viral Load Kinetics of Severe Acute Respiratory Syndrome Coronavirus 2 in Hospitalized Individuals With Coronavirus Disease 2019.

• DOI: 10.1093/ofid/ofab153
• 发表时间: 2021-08
• 期刊: Open forum infectious diseases
• 影响因子: 4.2
• 作者: Regan J;Flynn JP;Rosenthal A;Jordan H;Li Y;Chishti R;Giguel F;Corry H;Coxen K;Fajnzylber J;Gillespie E;Kuritzkes DR;Hacohen N;Goldberg MB;Filbin MR;Yu XG;Baden L;Ribeiro RM;Perelson AS;Conway JM;Li JZ;MGH COVID-19 Collection & Processing Teams
• 通讯作者: MGH COVID-19 Collection & Processing Teams