Mass gatherings as natural experiments: travel pulses reveal determinants of SARS-CoV-2 epidemic synchrony and predictability in U.S. states and counties

大规模集会作为自然实验：旅行脉搏揭示了美国各州和县 SARS-CoV-2 流行病同步性和可预测性的决定因素

• 批准号: 10510014
• 负责人: Pejman Rohani
• 金额: $ 24.01万
• 依托单位: UNIVERSITY OF GEORGIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10510014
• 关键词: 2019-nCoV; Affect; Age; Attention; Biological Models; COVID-19; COVID-19 pandemic; Communities; Complex; Contact Tracing; County; Data; Data Analytics; Demographic Factors; Demography; Disease; Ecology; Economics; Epidemic; Epidemiology; Evaluation; Event; Florida; Future; Geographic Factor; Geography; Goals; Health Personnel; Health Resources; Human; Immune; Immunity; Incidence; Individual; Infection; Intervention; Location; Maps; Modeling; Motorcycles; Natural experiment; New York; Physiologic pulse; Policies; Politics; Public Health; Readiness; Recording of previous events; Regional Disease; Research; Research Personnel; Risk; Rural; Series; Socioeconomic Factors; Statistical Models; Structure; System; Testing; Time; Time trend; Travel; Vaccination; Vaccines; Variant; Work; dynamic system; geographic difference; hospital bed; improved; novel; pandemic disease; response; skills; social; socioeconomics; suburb; transmission process; trend

Project Summary Variation in epidemic timing and intensity among U.S. communities has exacerbated shortages of vital pub- lic health resources, from testing and contact tracing to hospital beds and health care workers. Understanding when and why communities are most vulnerable to epidemic disease remains a critical unmet need in this and future epidemics. Our study will leverage a set of five mass gatherings (e.g., the Sturgis Motorcycle Ral- lies in 2020 and 2021) to assess the variable impact of travel pulses across a spectrum of U.S. communities. Our study is organized around two principle aims: Aim 1. To quantify how disease incidence in U.S. counties responds to travel pulses from focal mass gatherings in 2020 and then identify predictors of differing responses. To achieve this, we will use statistical models to quantify counties' differing response to travelers as a function of urbanicity, geographic isolation, age structure, and economic status. Aim 2. To quantify how disease predictability and connec- tivity vary among U.S. counties across 2020 and 2021. We will construct and parameterize mechanistic models of county-level transmission, and then assess model forecasting skill over short-term time horizons. This research will provide much-needed national and state summaries detailing which forces most strongly affect disease connectivity and predictability. Our work will provide detailed evaluations of individual counties, including publicly accessible interactive maps illustrating the relative effects of geography, demography, infection history, and vaccination. These products will help stakeholders identify at-risk communities and tailor control efforts to local conditions, and will highlight regions where increased surveillance is warranted.

项目摘要 美国社区之间流行时机和强度的差异加剧了重要的不足 LIC健康资源，从测试和联系跟踪到医院病床和医疗保健工作者。理解 社区何时以及为什么最容易受到流行病仍然是一个至关重要的未满足需求 和未来的流行病。我们的研究将利用一组五次大规模聚会（例如，Sturgis摩托车ral- 位于2020年和2021年），以评估旅行脉冲在美国社区中的可变影响。我们的 研究围绕两个原则进行组织：目标1。量化美国县疾病发生率的反应 在2020年从焦点聚集中旅行脉冲，然后确定不同反应的预测指标。为此， 我们将使用统计模型来量化县对旅行者的不同反应，这是城市化的函数， 地理隔离，年龄结构和经济状况。目的2。量化疾病可预测性和连通性如何 在2020年和2021年的美国县之间的关系各不相同。我们将构建和参数化机械模型 县级传输，然后评估在短期时间内的模型预测技能。这项研究 将提供急需的国家和州摘要，以限制最大的疾病影响 连接性和可预测性。我们的工作将提供对个别县的详细评估，包括 公开访问的互动地图说明了地理，人口统计学，感染历史的相对影响， 和疫苗接种。这些产品将帮助利益相关者确定处于危险的社区，并量身定制控制努力 当地条件，并将重点介绍有必要增加监视的地区。