Dynamic panel data modeling and surveillance of COVID-19 in metropolitan areas in the United States: Longitudinal trend analysis

Theresa B. Oehmke*, Lori A. Post, Charles B. Moss, Tariq Z. Issa, Michael J. Boctor, Sarah B. Welch, James F. Oehmke

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article numbere26081
JournalJournal of medical Internet research
Volume23
Issue number2
DOIs
StatePublished - Feb 2021

Keywords

  • Arellano-bond estimator
  • Atlanta
  • Baltimore
  • Boston
  • COVID-19
  • COVID-19 7-day lag
  • COVID-19 cities
  • COVID-19 metropolitan areas
  • COVID-19 transmission deceleration
  • COVID-19 transmission jerk
  • Charlotte
  • Chicago
  • Dallas
  • Denver
  • Detroit
  • Dynamic panel data
  • GMM
  • Generalized method of moments
  • Generalized method of the moments
  • Global COVID-19 surveillance
  • Houston
  • Los Angeles
  • Miami
  • Minneapolis
  • New York City
  • Orlando
  • Philadelphia
  • Phoenix
  • Portland
  • Riverside
  • SARS-CoV-2
  • SARS-CoV-2 surveillance
  • San Antonio
  • San Diego
  • San Francisco
  • Seattle
  • Second wave
  • St Louis
  • Tampa
  • US COVID-19
  • US COVID-19 surveillance system
  • US COVID-19 transmission acceleration
  • US COVID-19 transmission speed
  • US SARS-CoV-2
  • US econometrics
  • US public health surveillance
  • US surveillance metrics
  • Washington, DC
  • Wave 2
  • Wave two

ASJC Scopus subject areas

  • Health Informatics

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