COVID-19 Pandemic Vulnerability

在与北卡罗来纳州州立大学的研究人员（大卫赖夫博士、斯凯拉·马维尔博士、弗雷德·赖特博士、周毅辉博士和宋昆成博士）以及德克萨斯A&M大学的研究人员（邱伟学博士和伊万·鲁辛博士）的合作下，我们开发了一个与COVD-19大流行病相关的仪表板。 战胜COVID-19大流行需要各级政府，从联邦和州机构到县卫生部门，做出充分知情、数据驱动的决策。为应对这一大流行病，正在收集大量数据集，以便开发预测模型和互动监测应用程序。然而，从疾病发病率到个人移动性和合并症的大量数据流令人难以驾驭，难以整合，并且难以沟通。综合这些不同的数据对决策者至关重要，特别是在州和地方一级，以有效地优先考虑资源，确定和解决关键的脆弱性，并评估和实施有效的干预措施。为应对这种情况，我们开发了一个COVID-19大流行脆弱性指数（PVI）仪表板（https：//covid19pvi.niehs.nih.gov/），用于交互式监控，该仪表板具有县级记分卡，可将关键脆弱性驱动因素、历史趋势数据和定量预测可视化，以支持地方层面的决策。 我们将美国县和州一级的数据集汇总为四个主要领域的12个关键指标：当前感染率（感染率、增加率）、基线人口集中度（日间密度/交通量、居住密度），目前的干预措施（社会距离、检测率）以及健康和环境脆弱性（易感人群、空气污染、年龄分布、合并症、健康差异和医院床位）。这12个指标（其中有些是联合收割机多个数据集的组合）在县一级被纳入一个植被脆弱性指数总评分，采用了以前用于地理空间优先次序和概况分析的方法。构成这些指标的单个数据流衡量公共卫生灾难的公认的一般脆弱性因素或与COVID-19疫情相关的新兴因素。 在开发PVI时，我们对基础数据进行了严格的统计建模，以增强响应行动的信心，实现定量分析和监测，并提供病例和死亡的短期预测。我们的建模工作将种族差异等因素与社会经济因素，卫生资源分配和合并症的校正联系起来，并强调可能解释空间分布的基于地点的风险和资源不足。 具体而言，进行了三种类型的建模工作，并定期更新。首先，累积病例和死亡相关结果的流行病学模型提供了对大流行病流行病学的深入了解。 其次，动态时间依赖模型提供了与国家级模型相似的结果估计，但具有县级分辨率。最后，贝叶斯机器学习方法提供数据驱动的短期预测。 本项目涉及人类冠状病毒、新型冠状病毒、COVID-19、严重急性呼吸综合征冠状病毒病、SARS冠状病毒、SARS-coronavirus-2、SARS-cov-2、SARS-cov 2、SARS-related coronavirus 2、严重急性呼吸综合征冠状病毒2、SARS-Associated Coronavirus、SARS-cov或SARS-related Coronavirus的研究。

In a collaborative effort with investigators at North Carolina State University (Drs. David Reif, Skylar Marvel, Fred Wright, Yihui Zhou, and Kuncheng Song), and Texas A&M (Drs. Weihsueh Chiu and Ivan Rusyn), we have developed a dashboard related to the COVD-19 Pandemic. Defeating the COVID-19 pandemic requires well-informed, data-driven decisions at all levels of government, from federal and state agencies to county health departments. Numerous datasets are being collected in response to the pandemic, enabling the development of predictive models and interactive monitoring applications. However, this multitude of data streamsfrom disease incidence to personal mobility and comorbiditiesis overwhelming to navigate, difficult to integrate, and challenging to communicate. Synthesizing these disparate data is crucial for decision-makers, particularly at the state and local levels, to prioritize resources efficiently, identify and address key vulnerabilities, and evaluate and implement effective interventions. To address this situation, we developed a COVID-19 Pandemic Vulnerability Index (PVI) Dashboard (https://covid19pvi.niehs.nih.gov/) for interactive monitoring that features a county-level Scorecard to visualize key vulnerability drivers, historical trend data, and quantitative predictions to support decision making at a local level. We assembled U.S. county- and state-level datasets into 12 key indicators across four major domains: current infection rates (infection prevalence, rate of increase), baseline population concentration (daytime density/traffic, residential density), current interventions (social distancing, testing rates), and health and environmental vulnerabilities (susceptible populations, air pollution, age distribution, comorbidities, health disparities, and hospital beds). These 12 indicators (some of which combine multiple datasets) are integrated at the county level into an overall PVI score, employing methods previously used for geospatial prioritization and profiling. The individual data streams comprising these indicators measure either well-established, general vulnerability factors for public health disasters or emerging factors relevant to the COVID-19 pandemic. In developing the PVI, we performed rigorous statistical modeling of the underlying data to augment confidence in responsive actions, enable quantitative analysis and monitoring, and provide short-term predictions of cases and deaths. Our modeling efforts contextualize factors such as racial differences with corrections for socioeconomic factors, health resource allocation, and co-morbidities, plus highlighting place-based risks and resource deficits that might explain spatial distributions. Specifically, three types of modeling efforts were performed and are regularly updated. First, epidemiological modeling on cumulative case- and death-related outcomes provides insights into the epidemiology of the pandemic. Second, dynamic time-dependent modeling provides similar outcome estimates as national-level models, but with county-level resolution. Finally, a Bayesian machine learning approach provides data-driven, short-term forecasts. This project involves research on human coronavirus, novel coronavirus, COVID-19, Severe Acute Respiratory Syndrome coronavirus disease, SARS coronavirus, SARS-coronavirus-2, SARS-cov-2, SARS-cov2, SARS-related coronavirus 2, Severe acute respiratory syndrome coronavirus 2, SARS-Associated Coronavirus, SARS-cov, or SARS-Related Coronavirus.