RAPID: Covid-19 Forecasting Models for Removal of Social Distancing Measures

鉴于2019冠状病毒病大流行的动态和不确定性，预测模型已被用于为美国和其他地方实施减少危害的社会距离政策提供信息。过早或过于广泛地放松社交距离措施，特别是在仍有许多阳性病例的州，将促进社区传播，有可能再次呈指数级增长。该提案将把人口密度和人类流动模式的测量纳入预测模型，以预测美国各州和整个国家未来的COVID-19病例和死亡人数。随着各地放宽或终止先前的距离政策，考虑人口密度和仔细跟踪人员流动，并将这些数据纳入预测模型，将为这些行动如何可能影响COVID-19的轨迹提供数据驱动的证据。从更广泛的影响来看，该项目将开发、完善和分享COVID-19建模工具，以预测放松社交距离措施的不同情况下的疾病趋势。结果将通过交互式开放获取在线可视化工具提供。除了有可能直接为政策提供信息外，该项目的结果还将产生对所有COVID-19建模工作有用的数据产品，从而使整个建模界受益，这些数据产品将在一个网站上提供，结果、数据、模型和其他最终产品可在该网站上开放获取。在这项工作中产生的新的建模工具和衍生数据产品将以两种形成方式加强对COVID-19感染和死亡率的预测和情景。首先，将使用人口密度和人员流动数据来研究：(i)自愿和强制的社会距离措施如何影响人口层面的流动，以及（ii）这些措施与Covid-19感染和死亡率的关系。第二，将扩大建模框架，根据目前放松社会距离措施的不同情况，对疾病重新出现进行预测。这一多阶段混合建模框架将死亡统计模型与一个量化个体从易感到暴露、感染然后恢复的比率（称为SEIR模型）的新组成部分相结合。这个建模平台将非常灵活，可以定期更新数据，并在新类型的数据可用时纳入有关冠状病毒驱动因素的数据。该RAPID奖由环境生物学部传染病生态和进化项目颁发，资金来自《冠状病毒援助、救济和经济安全法案》。该奖项反映了美国国家科学基金会的法定使命，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。

Given the dynamic and uncertain nature of the COVID-19 pandemic, predictive models have been used to inform the implementation of harm-reducing social distancing policies in the United States and elsewhere. Relaxing social distancing measures too early or too broadly, particularly in states where there are still many positive cases, will facilitate community transmission with potential to return to exponential resurgence. This proposal will incorporate measures of population density and human mobility patterns into predictive models to project future COVID-19 cases and deaths within US states and the nation as a whole. As locations ease or end prior distancing policies, consideration of population density and careful tracking of human mobility and incorporation of these data into predictive models will provide data-driven evidence for how these actions could potentially affect COVID-19 trajectories. As a broader impact this project will develop, refine, and share COVID-19 modeling tools that will forecast disease trends for different scenarios of relaxed social distancing measures. Results will be made available through an interactive open-access online visualization tool. In addition to the potential to directly inform policy, the results of this project will benefit the modeling community at large by generating data products of utility to all COVID-19 modeling efforts, which will be made available on a website where results, data, models, and other resultant products are available for open access.The new modelling tools and derived data products produced in this work will enhance forecasting and scenarios of COVID-19 infections and mortality in two formative ways. First, data on population density and human mobility will be used to examine (i) how voluntary and mandated social distancing measures affect population-level mobility, and (ii) the relationship of these measures with Covid-19 infection and mortality. Second, a modelling framework will be expanded to generate forecasts of disease reemergence based on different scenarios of relaxation of social distancing measures that are currently in place. This multi-stage hybrid modeling framework combines a statistical model on deaths with a new component quantifying rates at which individuals move from being susceptible to exposed, infected, and then recovered (known as SEIR models). This modeling platform will be flexible to allow regular data updates and to incorporate new types of data on the drivers of coronavirus as they become available.This RAPID award is made by the Ecology and Evolution of Infectious Diseases Program in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.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.