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

BACKGROUND: Despite the availability of vaccines, the US incidence of new COVID-19 cases per day nearly doubled from the beginning of July to the end of August 2021, fueled largely by the rapid spread of the Delta variant. While the “Delta wave” appears to have peaked nationally, some states and mun...

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Autores principales: Oehmke, Theresa B, Moss, Charles B, Oehmke, James F
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8914733/
https://www.ncbi.nlm.nih.gov/pubmed/34882569
http://dx.doi.org/10.2196/28737
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author Oehmke, Theresa B
Moss, Charles B
Oehmke, James F
author_facet Oehmke, Theresa B
Moss, Charles B
Oehmke, James F
author_sort Oehmke, Theresa B
collection PubMed
description BACKGROUND: Despite the availability of vaccines, the US incidence of new COVID-19 cases per day nearly doubled from the beginning of July to the end of August 2021, fueled largely by the rapid spread of the Delta variant. While the “Delta wave” appears to have peaked nationally, some states and municipalities continue to see elevated numbers of new cases. Vigilant surveillance including at a metropolitan level can help identify any reignition and validate continued and strong public health policy responses in problem localities. OBJECTIVE: This surveillance report aimed to provide up-to-date information for the 25 largest US metropolitan areas about the rapidity of descent in the number of new cases following the Delta wave peak, as well as any potential reignition of the pandemic associated with declining vaccine effectiveness over time, new variants, or other factors. METHODS: COVID-19 pandemic dynamics for the 25 largest US metropolitan areas were analyzed through September 19, 2021, using novel metrics of speed, acceleration, jerk, and 7-day persistence, calculated from the observed data on the cumulative number of cases as reported by USAFacts. Statistical analysis was conducted using dynamic panel data models estimated with the Arellano-Bond regression techniques. The results are presented in tabular and graphic forms for visual interpretation. RESULTS: On average, speed in the 25 largest US metropolitan areas declined from 34 new cases per day per 100,000 population, during the week ending August 15, 2021, to 29 new cases per day per 100,000 population, during the week ending September 19, 2021. This average masks important differences across metropolitan areas. For example, Miami’s speed decreased from 105 for the week ending August 15, 2021, to 40 for the week ending September 19, 2021. Los Angeles, San Francisco, Riverside, and San Diego had decreasing speed over the sample period and ended with single-digit speeds for the week ending September 19, 2021. However, Boston, Washington DC, Detroit, Minneapolis, Denver, and Charlotte all had their highest speed of the sample during the week ending September 19, 2021. These cities, as well as Houston and Baltimore, had positive acceleration for the week ending September 19, 2021. CONCLUSIONS: There is great variation in epidemiological curves across US metropolitan areas, including increasing numbers of new cases in 8 of the largest 25 metropolitan areas for the week ending September 19, 2021. These trends, including the possibility of waning vaccine effectiveness and the emergence of resistant variants, strongly indicate the need for continued surveillance and perhaps a return to more restrictive public health guidelines for some areas.
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spelling pubmed-89147332022-03-12 COVID-19 Surveillance Updates in US Metropolitan Areas: Dynamic Panel Data Modeling Oehmke, Theresa B Moss, Charles B Oehmke, James F JMIR Public Health Surveill Original Paper BACKGROUND: Despite the availability of vaccines, the US incidence of new COVID-19 cases per day nearly doubled from the beginning of July to the end of August 2021, fueled largely by the rapid spread of the Delta variant. While the “Delta wave” appears to have peaked nationally, some states and municipalities continue to see elevated numbers of new cases. Vigilant surveillance including at a metropolitan level can help identify any reignition and validate continued and strong public health policy responses in problem localities. OBJECTIVE: This surveillance report aimed to provide up-to-date information for the 25 largest US metropolitan areas about the rapidity of descent in the number of new cases following the Delta wave peak, as well as any potential reignition of the pandemic associated with declining vaccine effectiveness over time, new variants, or other factors. METHODS: COVID-19 pandemic dynamics for the 25 largest US metropolitan areas were analyzed through September 19, 2021, using novel metrics of speed, acceleration, jerk, and 7-day persistence, calculated from the observed data on the cumulative number of cases as reported by USAFacts. Statistical analysis was conducted using dynamic panel data models estimated with the Arellano-Bond regression techniques. The results are presented in tabular and graphic forms for visual interpretation. RESULTS: On average, speed in the 25 largest US metropolitan areas declined from 34 new cases per day per 100,000 population, during the week ending August 15, 2021, to 29 new cases per day per 100,000 population, during the week ending September 19, 2021. This average masks important differences across metropolitan areas. For example, Miami’s speed decreased from 105 for the week ending August 15, 2021, to 40 for the week ending September 19, 2021. Los Angeles, San Francisco, Riverside, and San Diego had decreasing speed over the sample period and ended with single-digit speeds for the week ending September 19, 2021. However, Boston, Washington DC, Detroit, Minneapolis, Denver, and Charlotte all had their highest speed of the sample during the week ending September 19, 2021. These cities, as well as Houston and Baltimore, had positive acceleration for the week ending September 19, 2021. CONCLUSIONS: There is great variation in epidemiological curves across US metropolitan areas, including increasing numbers of new cases in 8 of the largest 25 metropolitan areas for the week ending September 19, 2021. These trends, including the possibility of waning vaccine effectiveness and the emergence of resistant variants, strongly indicate the need for continued surveillance and perhaps a return to more restrictive public health guidelines for some areas. JMIR Publications 2022-02-24 /pmc/articles/PMC8914733/ /pubmed/34882569 http://dx.doi.org/10.2196/28737 Text en ©Theresa B Oehmke, Charles B Moss, James F Oehmke. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 24.02.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.
spellingShingle Original Paper
Oehmke, Theresa B
Moss, Charles B
Oehmke, James F
COVID-19 Surveillance Updates in US Metropolitan Areas: Dynamic Panel Data Modeling
title COVID-19 Surveillance Updates in US Metropolitan Areas: Dynamic Panel Data Modeling
title_full COVID-19 Surveillance Updates in US Metropolitan Areas: Dynamic Panel Data Modeling
title_fullStr COVID-19 Surveillance Updates in US Metropolitan Areas: Dynamic Panel Data Modeling
title_full_unstemmed COVID-19 Surveillance Updates in US Metropolitan Areas: Dynamic Panel Data Modeling
title_short COVID-19 Surveillance Updates in US Metropolitan Areas: Dynamic Panel Data Modeling
title_sort covid-19 surveillance updates in us metropolitan areas: dynamic panel data modeling
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8914733/
https://www.ncbi.nlm.nih.gov/pubmed/34882569
http://dx.doi.org/10.2196/28737
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