Dynamic Panel Estimates of SARS-CoV-2 Infection Rates: Health Surveillance Informs Public Health Policy

BACKGROUND: The SARS-CoV-2 novel corona virus that causes COVID-19 is a global pandemic with higher mortality and morbidity than any other virus in a hundred years Our leaders are blind to where and how the disease is accelerating, decelerating, and shifting without public health surveillance Unfortunately, existing models of COVID-19 contagion rely on parameters such as R0 and use static statistical methods that do not capture all of the relevant dynamics needed for surveillance Existing surveillance uses data that are subject to significant measurement error and other contaminants OBJECTIVE: To provide a proof of concept on how to create surveillance metrics that correct for measurement error and data contamination to determine when it is safe to reopen This study applies state-of-the-art statistical modeling applied to existing data on the internet to derive the best available estimates of the state-level dynamics of COVID-19 infection in the U S METHODS: Dynamic panel data (DPD) models are estimated with the Arellano-Bond estimator utilizing the Generalized Method of Methods (GMM) This statistical technique allows for control of a variety of deficiencies in the existing dataset Tests of the validity of the model and statistical technique are applied RESULTS: The results indicate that the statistical approach is valid, as indicated by a Wald chi-square test of no explanatory power (chi2 (10) = 1,489 84 P< 001) and a Sargan chi-square test that the model identification is valid (chi2 (946) = 935 52, P= 59) The seven-day persistence rate for the week of 6/27-7/03 was 5188 (P< 001), meaning that every 10,000 new cases in the prior week is associated with 5,188 cases seven days later For the week of 7/04-7/10 the seven-day persistence rate increased by 2691 (P= 003), meaning that every 10,000 new cases in the prior week is associated with 7,879 new cases seven days later Applied to the reported number of cases, this is an increase of almost 100 additional new cases per day per state for the week of 7/04-7/10 This increase signifies an increase in the R or reproduction parameter in contagion models, and corroborates the hypothesis that re-opening of economies without best public health practices is associated with a resurgence of the pandemic CONCLUSIONS: DPD models successfully correct for measurement error and data contamination and are useful to derive surveillance metrics Opening America comes with two certainties: 1) we will be COVID-free only when there is an effective vaccine;and 2) the "social" end is going to occur before the "medical" end of the pandemic, therefore, we need improved surveillance metrics to inform leaders how to open sections of America more safely DPD models inform opening America in combination with the extraction of COVID data from existing websites CLINICALTRIAL: