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Accepted for/Published in: JMIR Public Health and Surveillance

Date Submitted: Mar 15, 2021
Date Accepted: Nov 30, 2021
Date Submitted to PubMed: Dec 9, 2021

The final, peer-reviewed published version of this preprint can be found here:

COVID-19 Surveillance Updates in US Metropolitan Areas: Dynamic Panel Data Modeling

Oehmke TB, Moss CB, Oehmke JF

COVID-19 Surveillance Updates in US Metropolitan Areas: Dynamic Panel Data Modeling

JMIR Public Health Surveill 2022;8(2):e28737

DOI: 10.2196/28737

PMID: 34882569

PMCID: 8914733

COVID-19 Surveillance Updates in U.S. Metropolitan Areas—A Dynamic Panel Data Modeling Approach: Is the ‘Delta wave’ over?

  • Theresa B Oehmke; 
  • Charles B Moss; 
  • James F Oehmke

ABSTRACT

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.


 Citation

Please cite as:

Oehmke TB, Moss CB, Oehmke JF

COVID-19 Surveillance Updates in US Metropolitan Areas: Dynamic Panel Data Modeling

JMIR Public Health Surveill 2022;8(2):e28737

DOI: 10.2196/28737

PMID: 34882569

PMCID: 8914733

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© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.

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