JMIR Publications

ABSTRACT

Background:

The Great Covid Shutdown is based on public health recommendations to eliminate SARS-CoV-2 or to flatten the curve. Governments at the country or sub-country level that failed to effectively shut down resulted in increases of Covid infections. The US has no national policy, leaving states to independently implement public health guidelines regarding closures, social distancing, masks, establishment capacity, crowd control, and hygiene. Reopening guidelines are predicated on a sustained decline in Covid, however, operationalization of ‘sustained decline’ varies by state and county. Existing models of Covid-19 contagion rely on parameters such as case estimates or R0 and use intensive data collection efforts. They use static statistical models that do not capture all of the relevant dynamics required to measure sustained declines. Moreover, existing Covid models use data that are subject to significant measurement error and other contaminants.

Objective:

This surveillance study applies state-of-the-art statistical modeling to existing data extracted from the internet state government tallies of Covid infections to calculate the best available estimates of the state-level dynamics of Covid-19 infection. This proof of concept surveillance study informs public health by providing a standardized metric of Covid contagion infections.

Methods:

Dynamic panel data (DPD) models are estimated with the Arellano-Bond estimator utilizing the Generalized Method of Moments. This statistical technique allows for control of a variety of deficiencies in the existing data. Tests of the validity of the model and statistical techniques are applied.

Results:

The results indicate 1) that the statistical approach is valid, including for determining recent changes in the pattern of infection, and 2) during the weeks of June 13th -19th and 20th-26th the evolution of the pandemic changed with greater inter-temporal persistence of the infection rate. This change represents an increase in the contagion model R value for those periods, and is consistent with a reemergence of the pandemic.

Conclusions:

Opening America comes with three certainties: 1) the “social” end of the pandemic and re-opening is going to occur before the “medical” end of the pandemic and perhaps even while the pandemic is growing, therefore, we need improved standardized surveillance techniques and policies to inform the public when it is safer to open sections of America; 2) varying public health policy and guidelines unnecessarily result in varying degrees of contagion and outbreaks; and 3) under current regimes and practices, even those states most successful in containing the pandemic are still seeing a small but constant stream of daily new cases.