Assessing the impact of COVID-19 interventions on human mobility and SARS-CoV-2 transmission dynamics in the United States

项目摘要 SARS-CoV-2的快速传播导致全球各国实施了强烈的社会距离和 采取封锁措施以减少传播。年新报告新冠肺炎病例和死亡人数第一个高峰 美国的检测发生在2020年4月，但阳性检测的数量在6月份再次开始增加，因为 许多州开始放松最初的就地避难令，尽管疫情仍在传播。在没有的情况下 广泛部署有效疫苗或其他药物干预，州和地方 政府将不得不依靠一系列非药物干预措施(NPI)来限制进一步的疫情爆发 在接下来的12-24个月内。为政策制定者提供有关 不同的NPI在一系列现实的流行病学背景下，我们将审查不同的NPI的影响 使用地理上现实的、基于代理的模型来研究流动模式和疾病传播。第一, 我们将从以下几个方面汇集地方、县和国家政策的全面数据库 公共网站和社交媒体，并按干预类型对这些政策进行分类。我们还将获得 来自几个不同公共数据库的流行病学数据，并使用县级病例、检测、 住院和死亡率数据，以评估不同的县和州政策和NPI在实时- 时间到了。 我们将使用来自移动电话的移动数据来评估NPI和SARS-CoV-2动态之间的联系 公开来源和与多个数据提供商的数据共享协议的组合。首先，我们 将使用统计模型来评估不同类别的县州新冠肺炎政策的影响 和与流行病学相关的人类流动性和活动模式的国家指标，包括不同国家的活动数据 名胜古迹受到与新冠肺炎相关的特别限制。然后将使用这些移动性指标 以通知我们基于代理的传播模型中本地联系模式的变化。这种传播模式 还将纳入有关人口统计、社会经济因素、并存疾病和 已被证明对SARS-CoV-2流行病学很重要的职业。当地人口将会是 使用从手机数据得出的区域连通性指标进行链接。结合这些细节将允许 美国在核算时估计不同政策对一系列设置中传播动态的影响 考虑到当地情况和区域动态。模型估计将每周迭代更新 在项目过程中提供感染、住院和死亡的短期预测 关于目前全国各地NPI的组合情况。这些预测将用于验证我们的NPI-Impact 通过将预测与未来的观测进行比较来进行估计。

Project Summary The rapid spread of SARS-CoV-2 led countries across the globe to implement strong social distancing and lockdown measures to reduce transmission. The first peak of newly reported COVID-19 cases and deaths in the United States occurred in April 2020, but the number of positive tests began increasing again in June as many states started easing their initial shelter-in-place orders despite ongoing transmission. In the absence of widespread deployment of an effective vaccine or another pharmaceutical intervention, state and local governments will have to rely on a range of non-pharmaceutical interventions (NPIs) to limit further outbreaks over the next 12-24 months. To provide policy makers with actionable information regarding the efficacy of different NPIs under a range of realistic epidemiological contexts, we will examine the impact of different NPIs on both mobility patterns and disease transmission using a geographically realistic, agent-based model. First, we will assemble a comprehensive database of local, county, and state policies related to COVID-19 from public websites and social media and categorize these policies by intervention type. We will also obtain epidemiological data from several different publicly available databases and use county-level case, testing, hospitalization, and mortality data to assess the impact of different county and state policies and NPIs in real- time. We will assess the link between NPIs and SARS-CoV-2 dynamics using cell phone-derived mobility data from a combination of publicly available sources and data sharing agreements with several data providers. First, we will use statistical models to assess the impact of different categories of county and state COVID-19 policies and NPIs on epidemiologically-relevant human mobility and activity patterns, including activity data at different places-of-interest subject to particular COVID-19 related restrictions. These mobility metrics will then be used to inform changes in local contact patterns in our agent-based transmission model. This transmission model will also incorporate detailed information on the demographics, socioeconomic factors, co-morbidities, and occupations that have been shown to be important for SARS-CoV-2 epidemiology. Local populations will be linked using regional connectivity metrics derived from cell phone data. Incorporation of these details will allow us to estimate the impact of different policies on transmission dynamics in a range of settings while accounting for local conditions as well as regional dynamics. Model estimates will be iteratively updated on a weekly basis over the course of the project to provide short-term forecasts of infections, hospitalizations, and deaths based on the current mix of NPIs across the country. These forecasts will be used to validate our NPI-impact estimates by comparing forecasts to future observations.