Dynamic Panel Surveillance of COVID-19 Transmission in the United States to Inform Health Policy: Observational Statistical Study

• Published in: Journal of medical Internet research 2020-10, Vol.22 (10), p.e21955
• Main Authors: Oehmke, James Francis, Moss, Charles B, Singh, Lauren Nadya, Oehmke, Theresa Bristol, Post, Lori Ann
• Format: Article
• Language: English
• Subjects: Betacoronavirus; Censuses; Change agents; Communicable Disease Control - legislation & jurisprudence; Communicable Disease Control - methods; Contact tracing; Coronavirus Infections - epidemiology; Coronavirus Infections - transmission; Coronaviruses; COVID-19; Data collection; Disease transmission; Epidemics; Geography; Health Policy; Health surveillance; Humans; Infections; Masks; Measurement; Models, Statistical; Mortality; Original Paper; Pandemics; Panel data; Parameter estimation; Pneumonia, Viral - epidemiology; Pneumonia, Viral - transmission; Population; Prevention; Public Health; Public Health Informatics - methods; Reference Standards; Regional variations; Regression Analysis; SARS-CoV-2; Severe acute respiratory syndrome; Severe acute respiratory syndrome coronavirus 2; Social distancing; State government; Surveillance systems; United States
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/21955

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 R 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 pandemi