RAPID: Inference, Forecasting, and Intervention Modeling of COVID-19

RAPID：COVID-19 的推理、预测和干预建模

• 批准号: 2027369
• 负责人: Jeffrey Shaman
• 金额: $ 19.86万
• 依托单位: Columbia 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: 2020-04-15 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027369&HistoricalAwards=false
• 关键词: RAPID; Inference; Forecasting; Intervention; Modeling

A novel coronavirus emerged in late 2019 and by the end of March 2020 had spread to more than 170 countries, causing more than 750,000 confirmed cases of COVID-19 disease and over 4000 deaths. There is an urgent need to better understand this rapidly evolving crisis to predict and mitigate its effects. The focus of this project is to apply mathematical and statistical models to: 1) estimate critical characteristics of the virus and its transmission; 2) forecast new COVID-19 outcomes; and 3) estimate the potential effectiveness of non-pharmaceutical (e.g. school closures, travel restrictions) interventions. Projections of COVID-19 cases, hospital bed demand and ventilator demand, will be communicated to the public, public health agencies and government officials as they are developed in order to support real-time policy decision making. Inference, forecasting, and intervention modeling of the COVID-19 outbreak in the US is needed to improve understanding of SARS-CoV2 transmission dynamics and to support COVID-19 response and intervention efforts. For this project, observations of reported infections in the US, in conjunction with commuting data, a networked dynamic metapopulation model and Bayesian inference, will be used to infer critical and evolving characteristics associated with SARS-CoV2 spread in the US, including the fraction of undocumented infections and their contagiousness. Using these same model-inference methods, ensemble projections of future COVID-19 incidence in the US will be developed and generated. Finally, models will be developed to simulate and study the effects of non-pharmaceutical interventions, including school closure, isolation, quarantine and travel restrictions, on COVID-19 incidence. Project findings will be communicated in real time to public health and government officials. This award is co-funded with the Ecology and Evolution of Infectious Diseases program (Division of Environmental Biology), the Applied Mathematics program (Division of Mathematical Sciences), and the Office of Multidisciplinary Activities (OMA) program.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.

一种新型冠状病毒于 2019 年底出现，截至 2020 年 3 月，已传播到 170 多个国家，导致超过 75 万例确诊的 COVID-19 病例和 4000 多人死亡。迫切需要更好地了解这场迅速演变的危机，以预测和减轻其影响。 该项目的重点是应用数学和统计模型来：1）估计病毒及其传播的关键特征； 2) 预测新的 COVID-19 结果； 3) 估计非药物干预措施（例如学校关闭、旅行限制）的潜在有效性。 COVID-19 病例、病床需求和呼吸机需求的预测将在制定后传达给公众、公共卫生机构和政府官员，以支持实时政策决策。需要对美国 COVID-19 疫情进行推断、预测和干预建模，以增进对 SARS-CoV2 传播动态的了解并支持 COVID-19 应对和干预工作。 在该项目中，对美国报告的感染情况的观察，结合通勤数据、网络动态集合种群模型和贝叶斯推理，将用于推断与 SARS-CoV2 在美国传播相关的关键和不断变化的特征，包括未记录感染的比例及其传染性。使用这些相同的模型推理方法，将开发和生成美国未来 COVID-19 发病率的整体预测。最后，将开发模型来模拟和研究非药物干预措施（包括学校关闭、隔离、检疫和旅行限制）对 COVID-19 发病率的影响。项目结果将实时传达给公共卫生和政府官员。 该奖项由传染病生态与进化项目（环境生物学部）、应用数学项目（数学科学部）和多学科活动办公室（OMA）项目共同资助。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力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

Burden and characteristics of COVID-19 in the United States during 2020

• DOI: 10.1038/s41586-021-03914-4
• 发表时间: 2021-08-26
• 期刊: NATURE
• 影响因子: 64.8
• 作者: Sen, Pei;Yamana, Teresa K.;Shaman, Jeffrey
• 通讯作者: Shaman, Jeffrey

Mask-wearing and control of SARS-CoV-2 transmission in the USA: a cross-sectional study.

• DOI: 10.1016/s2589-7500(20)30293-4
• 发表时间: 2021-03
• 期刊: The Lancet. Digital health
• 作者: Rader B;White LF;Burns MR;Chen J;Brilliant J;Cohen J;Shaman J;Brilliant L;Kraemer MUG;Hawkins JB;Scarpino SV;Astley CM;Brownstein JS
• 通讯作者: Brownstein JS