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

新型冠状病毒于二零一九年年底出现，截至二零二零年三月底已蔓延至170多个国家，导致逾75万宗COVID-19确诊病例及逾4，000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;和3）估计非药物（例如）的潜在有效性关闭学校、旅行限制）干预措施。 COVID-19病例、医院床位需求和呼吸机需求的预测将在制定时传达给公众、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，以提高对SARS-CoV 2传播动态的理解，并支持COVID-19应对和干预工作。 对于这个项目，在美国报告的感染的观察，结合通勤数据，网络动态集合种群模型和贝叶斯推理，将被用来推断与SARS-CoV 2在美国传播相关的关键和不断变化的特征，包括未记录的感染的比例及其传染性。使用这些相同的模型推断方法，将开发并生成美国未来COVID-19发病率的总体预测。最后，将开发模型来模拟和研究非药物干预措施对COVID-19发病率的影响，包括关闭学校、隔离、检疫和旅行限制。项目调查结果将真实的及时传达给公共卫生和政府官员。 该奖项是与生态学和传染病进化计划（环境生物学部），应用数学计划（数学科学部），和多学科活动办公室（OMA）计划共同资助的。该奖项反映了NSF的法定使命，并已被认为是值得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的支持。

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.