Dynamic Panel Surveillance of COVID-19 Transmission in the United States to Inform Health Policy: Observational Statistical Study.
Dynamic Panel Surveillance of COVID-19 Transmission in the United States to Inform Health Policy: Observational Statistical Study.
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DOI：
10.2196/21955
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发表时间：
2020-10-05
影响因子：
7.4
通讯作者：
Post LA
中科院分区：
文献类型：
--
作者：
Oehmke JF;Moss CB;Singh LN;Oehmke TB;Post LA
大COVID-19疫苗旨在消除或减缓导致COVID-19的SARS-CoV-2病毒的传播。美国没有全国性的政策，让各州独立实施公共卫生指导方针，这些指导方针以COVID-19病例持续下降为前提。"持续下降"的操作化因州、县而异。COVID-19传播的现有模型依赖于病例估计或R0等参数，并依赖于密集的数据收集工作。静态统计模型无法捕捉衡量持续下降所需的所有相关动态。此外，现有COVID-19模型使用的数据存在重大测量误差和污染。这项研究将使用州政府对SARS-CoV-2感染的统计，包括SARS-CoV-2感染的州一级动态，产生COVID-19传播速度的速度、加速度、加加速度和7天滞后的新指标。这项研究提供了一个全球监测系统的原型，为公共卫生实践提供信息，包括新的COVID-19传播标准化指标，与传统的监测工具结合使用。动态面板数据模型估计与Arellano-Bond估计使用广义矩法。这种统计技术可以控制现有数据中的各种缺陷。模型和统计技术的有效性进行了测试。根据回归结果验证了统计方法，该结果确定了感染模式的近期变化。在2020年8月17日至23日和8月24日至30日的几周内，美国大流行的演变存在很大的区域差异。人口普查区域1和2相对平静，具有较小但显著的持续性效应，与前2周相比保持相对不变。人口普查区3对所进行的检测数量很敏感，病例率一直很高。每周一次的特别分析显示，这些结果是由大学阳性检测报告数量较多的州推动的。在8月24日至30日的一周内，第4普查区的病例数保持不变，持续性效应显著增加。这一变化代表了该周传播模型R值的增加，与大流行的重新出现一致。美国重新开放伴随着三个问题：（1）疫情的"社会"结束，即使疫情正在加剧，重新开放也将在"医疗"结束之前发生。我们需要改进标准化的监测技术，以告知领导人何时开放该国的部分地区是安全的;（2）不同的公共卫生政策和指导方针不必要地导致不同程度的传播和爆发;（3）即使是那些在控制流行病方面最成功的州，每天也会继续出现少量但持续不断的新病例。
The Great COVID-19 Shutdown aimed to eliminate or slow the spread of SARS-CoV-2, the virus that causes COVID-19. The United States has no national policy, leaving states to independently implement public health guidelines that are predicated on a sustained decline in COVID-19 cases. Operationalization of “sustained decline” varies by state and county. Existing models of COVID-19 transmission rely on parameters such as case estimates or R0 and are dependent on intensive data collection efforts. Static statistical models do not capture all of the relevant dynamics required to measure sustained declines. Moreover, existing COVID-19 models use data that are subject to significant measurement error and contamination. This study will generate novel metrics of speed, acceleration, jerk, and 7-day lag in the speed of COVID-19 transmission using state government tallies of SARS-CoV-2 infections, including state-level dynamics of SARS-CoV-2 infections. This study provides the prototype for a global surveillance system to inform public health practice, including novel standardized metrics of COVID-19 transmission, for use in combination with traditional surveillance tools. Dynamic panel data models were estimated with the Arellano-Bond estimator using the generalized method of moments. This statistical technique allows for the control of a variety of deficiencies in the existing data. Tests of the validity of the model and statistical techniques were applied. The statistical approach was validated based on the regression results, which determined recent changes in the pattern of infection. During the weeks of August 17-23 and August 24-30, 2020, there were substantial regional differences in the evolution of the US pandemic. Census regions 1 and 2 were relatively quiet with a small but significant persistence effect that remained relatively unchanged from the prior 2 weeks. Census region 3 was sensitive to the number of tests administered, with a high constant rate of cases. A weekly special analysis showed that these results were driven by states with a high number of positive test reports from universities. Census region 4 had a high constant number of cases and a significantly increased persistence effect during the week of August 24-30. This change represents an increase in the transmission model R value for that week and is consistent with a re-emergence of the pandemic. Reopening the United States comes with three certainties: (1) the “social” end of the pandemic and reopening are going to occur before the “medical” end even while the pandemic is growing. We need improved standardized surveillance techniques to inform leaders when it is safe to open sections of the country; (2) varying public health policies and guidelines unnecessarily result in varying degrees of transmission and outbreaks; and (3) even those states most successful in containing the pandemic continue to see a small but constant stream of new cases daily.
影响因子：
2.8
作者：
Ahn DG;Shin HJ;Kim MH;Lee S;Kim HS;Myoung J;Kim BT;Kim SJ
通讯作者：
Kim SJ
影响因子：
3.8
作者：
Aslund, Anders
通讯作者：
Aslund, Anders