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 US 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 at the metropolitan area scale. 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 metropolitan area values for novel indicators to measure the transmission of disease.
METHODS
Using a longitudinal trend analysis study design, we extracted 260 days of COVID data from public health registries. We use an empirical difference equation to measure the daily number of cases in the 25 largest U.S. 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 (GMM) approach by implementing the Arellano-Bond estimator in R.
RESULTS
Most recently, Minneapolis and Chicago have the greatest average number of daily new positive results per standardized 100,000 population, which we call speed. Extreme behavior in Minneapolis showed speed jumping from 17 to 30 (67%) in one 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 one 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 US at the beginning of their cold weather season. With these persistence effects, and indoor activities becoming more popular as 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.
COVID-19 Surveillance Updates in U.S. Metropolitan Areas—A Dynamic Panel Data Modeling Approach: Is the ‘Delta wave’ over? (Preprint)
