Background 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. Objective 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. Methods 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. Results 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. Conclusions 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.
Background SARS-CoV-2, the novel coronavirus that causes COVID-19, is a global pandemic with higher mortality and morbidity than any other virus in the last 100 years. Without public health surveillance, policy makers cannot know where and how the disease is accelerating, decelerating, and shifting. Unfortunately, existing models of COVID-19 contagion rely on parameters such as the basic reproduction number and use static statistical methods that do not capture all the relevant dynamics needed for surveillance. Existing surveillance methods use data that are subject to significant measurement error and other contaminants. Objective The aim of this study is to provide a proof of concept of the creation of surveillance metrics that correct for measurement error and data contamination to determine when it is safe to ease pandemic restrictions. We applied state-of-the-art statistical modeling to existing internet data to derive the best available estimates of the state-level dynamics of COVID-19 infection in the United States. Methods Dynamic panel data (DPD) models were estimated with the Arellano-Bond estimator using the generalized method of moments. This statistical technique enables control of various deficiencies in a data set. The validity of the model and statistical technique was tested. Results A Wald chi-square test of the explanatory power of the statistical approach indicated that it is valid (χ210=1489.84, P<.001), and a Sargan chi-square test indicated that the model identification is valid (χ2946=935.52, P=.59). The 7-day persistence rate for the week of June 27 to July 3 was 0.5188 (P<.001), meaning that every 10,000 new cases in the prior week were associated with 5188 cases 7 days later. For the week of July 4 to 10, the 7-day persistence rate increased by 0.2691 (P=.003), indicating that every 10,000 new cases in the prior week were associated with 7879 new cases 7 days later. Applied to the reported number of cases, these results indicate an increase of almost 100 additional new cases per day per state for the week of July 4-10. This signifies an increase in the reproduction parameter in the contagion models and corroborates the hypothesis that economic reopening without applying 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. The opening of America involves two certainties: the country will be COVID-19–free only when there is an effective vaccine, and the “social” end of the pandemic will occur before the “medical” end. Therefore, improved surveillance metrics are needed to inform leaders of how to open sections of the United States more safely. DPD models can inform this reopening in combination with the extraction of COVID-19 data from existing websites.
Background The COVID-19 pandemic has had profound and differential impacts on metropolitan areas across the United States and around the world. Within the United States, metropolitan areas that were hit earliest with the pandemic and reacted with scientifically based health policy were able to contain the virus by late spring. For other areas that kept businesses open, the first wave in the United States hit in mid-summer. As the weather turns colder, universities resume classes, and people tire of lockdowns, a second wave is ascending in both metropolitan and rural areas. It becomes more obvious that additional SARS-CoV-2 surveillance is needed at the local level to track recent shifts in the pandemic, rates of increase, and persistence. Objective The goal of this study is to provide advanced surveillance metrics for COVID-19 transmission that account for speed, acceleration, jerk and persistence, and weekly shifts, to better understand and manage risk in metropolitan areas. Existing surveillance measures coupled with our dynamic metrics of transmission will inform health policy to control the COVID-19 pandemic until, and after, an effective vaccine is developed. Here, we provide values for novel indicators to measure COVID-19 transmission at the metropolitan area level. Methods Using a longitudinal trend analysis study design, we extracted 260 days of COVID-19 data from public health registries. We used an empirical difference equation to measure the daily number of cases in the 25 largest US metropolitan areas as a function of the prior number of cases and weekly shift variables based on a dynamic panel data model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. Results Minneapolis and Chicago have the greatest average number of daily new positive results per standardized 100,000 population (which we refer to as speed). Extreme behavior in Minneapolis showed an increase in speed from 17 to 30 (67%) in 1 week. The jerk and acceleration calculated for these areas also showed extreme behavior. The dynamic panel data model shows that Minneapolis, Chicago, and Detroit have the largest persistence effects, meaning that new cases pertaining to a specific week are statistically attributable to new cases from the prior week. Conclusions Three of the metropolitan areas with historically early and harsh winters have the highest persistence effects out of the top 25 most populous metropolitan areas in the United States at the beginning of their cold weather season. With these persistence effects, and with indoor activities becoming more popular as the weather gets colder, stringent COVID-19 regulations will be more important than ever to flatten the second wave of the pandemic. As colder weather grips more of the nation, southern metropolitan areas may also see large spikes in the number of cases.
BACKGROUND COVID-19 cases in U.S. metropolitan areas fell dramatically in January and early February, but flattened out in the last two weeks of February and first week of March. The reopening of states and municipalities coupled with the emergence of SARS-Cov-2 variants raises the specter of a re-ignition of explosive growth. Vigilant surveillance can help identify any re-ignition and validate an early and strong public health policy response. OBJECTIVE This surveillance reports aimed to provide up to date information about a potential re-ignition of the pandemic using the novel metrics of speed, acceleration, jerk, and 7-day persistence. METHODS COVID-19 pandemic dynamics for the 25 largest U.S. metropolitan areas were analyzed through 3/7/2021 using the novel metrics calculated on the basis of observed data on the cumulative number of cases as reported in usafacts.org. Statistical analysis was conducted using dynamic panel data models estimated with Arellano-Bond regression techniques. Results are presented in tabular and graphic forms for visual interpretation. RESULTS On average, speed in the 25 largest U.S. metropolitan areas declined from 68 new cases per day per 100,000 population during the week of 1/4-1/10/21 to 20 during the week of 3/1-3/7/2021. However, the decline stagnated and speed dropped only one case per day over the past two weeks from a value of 21 during the week of 2/15-2/21/2021. This stagnating decline is confirmed by acceleration and jerk data. Houston exhibited a smaller than average overall decline in speed and a bounce off its low speed of 17 during the week of 2/15-2/21/2021 to 27 during the week of 2/22-2/28-2021 and 29 during the week of 3/1/-3/7/2011. CONCLUSIONS The stagnation is evidence of the persistence of the pandemic and the possibility of a surge in new cases and possibly explosive growth as states reopen and if people choose not to follow recommended guidelines including social distancing and face mask wearing. Our evidence that Houston showed signs of a bounce during the last week of February and the first week of March coupled with the presence of all the major SARS-Cov-2 variants in the metropolitan area strongly suggests that reopening will lead to an upsurge in Houston’s COVID-19 cases with the potential for re-igniting exponential growth.
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.
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