Modeling Effects of Spatial Heterogeneities and Layered Exposure Interventions on the Spread of COVID-19 across New Jersey
COVID-19 created an unprecedented global public health crisis during 2020–2021. The severity of the fast-spreading infection, combined with uncertainties regarding the physical and biological processes affecting transmission of SARS-CoV-2, posed enormous challenges to healthcare systems. Pandemic dy...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8618648/ https://www.ncbi.nlm.nih.gov/pubmed/34831706 http://dx.doi.org/10.3390/ijerph182211950 |
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author | Ren, Xiang Weisel, Clifford P. Georgopoulos, Panos G. |
author_facet | Ren, Xiang Weisel, Clifford P. Georgopoulos, Panos G. |
author_sort | Ren, Xiang |
collection | PubMed |
description | COVID-19 created an unprecedented global public health crisis during 2020–2021. The severity of the fast-spreading infection, combined with uncertainties regarding the physical and biological processes affecting transmission of SARS-CoV-2, posed enormous challenges to healthcare systems. Pandemic dynamics exhibited complex spatial heterogeneities across multiple scales, as local demographic, socioeconomic, behavioral and environmental factors were modulating population exposures and susceptibilities. Before effective pharmacological interventions became available, controlling exposures to SARS-CoV-2 was the only public health option for mitigating the disease; therefore, models quantifying the impacts of heterogeneities and alternative exposure interventions on COVID-19 outcomes became essential tools informing policy development. This study used a stochastic SEIR framework, modeling each of the 21 New Jersey counties, to capture important heterogeneities of COVID-19 outcomes across the State. The models were calibrated using confirmed daily deaths and SQMC optimization and subsequently applied in predictive and exploratory modes. The predictions achieved good agreement between modeled and reported death data; counterfactual analysis was performed to assess the effectiveness of layered interventions on reducing exposures to SARS-CoV-2 and thereby fatality of COVID-19. The modeling analysis of the reduction in exposures to SARS-CoV-2 achieved through concurrent social distancing and face-mask wearing estimated that 357 [IQR (290, 429)] deaths per 100,000 people were averted. |
format | Online Article Text |
id | pubmed-8618648 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-86186482021-11-27 Modeling Effects of Spatial Heterogeneities and Layered Exposure Interventions on the Spread of COVID-19 across New Jersey Ren, Xiang Weisel, Clifford P. Georgopoulos, Panos G. Int J Environ Res Public Health Article COVID-19 created an unprecedented global public health crisis during 2020–2021. The severity of the fast-spreading infection, combined with uncertainties regarding the physical and biological processes affecting transmission of SARS-CoV-2, posed enormous challenges to healthcare systems. Pandemic dynamics exhibited complex spatial heterogeneities across multiple scales, as local demographic, socioeconomic, behavioral and environmental factors were modulating population exposures and susceptibilities. Before effective pharmacological interventions became available, controlling exposures to SARS-CoV-2 was the only public health option for mitigating the disease; therefore, models quantifying the impacts of heterogeneities and alternative exposure interventions on COVID-19 outcomes became essential tools informing policy development. This study used a stochastic SEIR framework, modeling each of the 21 New Jersey counties, to capture important heterogeneities of COVID-19 outcomes across the State. The models were calibrated using confirmed daily deaths and SQMC optimization and subsequently applied in predictive and exploratory modes. The predictions achieved good agreement between modeled and reported death data; counterfactual analysis was performed to assess the effectiveness of layered interventions on reducing exposures to SARS-CoV-2 and thereby fatality of COVID-19. The modeling analysis of the reduction in exposures to SARS-CoV-2 achieved through concurrent social distancing and face-mask wearing estimated that 357 [IQR (290, 429)] deaths per 100,000 people were averted. MDPI 2021-11-14 /pmc/articles/PMC8618648/ /pubmed/34831706 http://dx.doi.org/10.3390/ijerph182211950 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ren, Xiang Weisel, Clifford P. Georgopoulos, Panos G. Modeling Effects of Spatial Heterogeneities and Layered Exposure Interventions on the Spread of COVID-19 across New Jersey |
title | Modeling Effects of Spatial Heterogeneities and Layered Exposure Interventions on the Spread of COVID-19 across New Jersey |
title_full | Modeling Effects of Spatial Heterogeneities and Layered Exposure Interventions on the Spread of COVID-19 across New Jersey |
title_fullStr | Modeling Effects of Spatial Heterogeneities and Layered Exposure Interventions on the Spread of COVID-19 across New Jersey |
title_full_unstemmed | Modeling Effects of Spatial Heterogeneities and Layered Exposure Interventions on the Spread of COVID-19 across New Jersey |
title_short | Modeling Effects of Spatial Heterogeneities and Layered Exposure Interventions on the Spread of COVID-19 across New Jersey |
title_sort | modeling effects of spatial heterogeneities and layered exposure interventions on the spread of covid-19 across new jersey |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8618648/ https://www.ncbi.nlm.nih.gov/pubmed/34831706 http://dx.doi.org/10.3390/ijerph182211950 |
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