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

鉴于新冠肺炎大流行的动态性和不确定性，预测性模型已被用于为美国和其他地方实施减少伤害的社会疏远政策提供信息。过早或过于广泛地放松社会距离措施，特别是在仍有许多积极病例的州，将促进社区传播，并有可能恢复指数级复苏。该提案将把人口密度和人口流动模式的衡量标准纳入预测模型，以预测美国各州和全国未来的新冠肺炎病例和死亡人数。随着地点放松或结束先前的疏远政策，考虑人口密度和仔细跟踪人类流动性，并将这些数据纳入预测模型，将为这些行动可能如何影响新冠肺炎的轨迹提供数据驱动的证据。作为更广泛的影响，该项目将开发、改进和共享新冠肺炎建模工具，这些工具将预测放松社会距离措施的不同场景下的疾病趋势。结果将通过交互式开放获取的在线可视化工具提供。除了有可能直接为政策提供信息外，该项目的结果还将通过生成对所有新冠肺炎建模工作有用的数据产品而使整个建模社区受益，这些数据产品将在一个网站上提供，在该网站上可以开放访问结果、数据、模型和其他结果产品。在这项工作中产生的新的建模工具和派生的数据产品将以两种形成的方式增强新冠肺炎感染和死亡率的预测和情景。首先，有关人口密度和人口流动性的数据将被用来检验(I)自愿和强制的社会疏远措施如何影响人口水平的流动性，以及(Ii)这些措施与新冠肺炎感染和死亡率的关系。第二，将扩大一个模型框架，根据目前实施的放宽社会疏远措施的不同情景，生成疾病复发的预测。这个多阶段混合建模框架结合了一个关于死亡的统计模型和一个新的组成部分，该模型量化了个体从易感到暴露、感染和然后恢复的比率(称为SEIR模型)。这个建模平台将灵活地允许定期数据更新，并在可用时纳入关于冠状病毒驱动因素的新类型数据。这一快速奖项由环境生物学部门的传染病生态学和进化计划作出，使用冠状病毒援助、救济和经济安全(CARE)法案的资金。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.