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A multi-method approach to modeling COVID-19 disease dynamics in the United States

In this paper, we proposed a multi-method modeling approach to community-level spreading of COVID-19 disease. Our methodology was composed of interconnected age-stratified system dynamics models in an agent-based modeling framework that allowed for a granular examination of the scale and severity of...

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Autores principales: Mokhtari, Amir, Mineo, Cameron, Kriseman, Jeffrey, Kremer, Pedro, Neal, Lauren, Larson, John
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8203660/
https://www.ncbi.nlm.nih.gov/pubmed/34127757
http://dx.doi.org/10.1038/s41598-021-92000-w
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author Mokhtari, Amir
Mineo, Cameron
Kriseman, Jeffrey
Kremer, Pedro
Neal, Lauren
Larson, John
author_facet Mokhtari, Amir
Mineo, Cameron
Kriseman, Jeffrey
Kremer, Pedro
Neal, Lauren
Larson, John
author_sort Mokhtari, Amir
collection PubMed
description In this paper, we proposed a multi-method modeling approach to community-level spreading of COVID-19 disease. Our methodology was composed of interconnected age-stratified system dynamics models in an agent-based modeling framework that allowed for a granular examination of the scale and severity of disease spread, including metrics such as infection cases, deaths, hospitalizations, and ICU usage. Model parameters were calibrated using an optimization technique with an objective function to minimize error associated with the cumulative cases of COVID-19 during a training period between March 15 and October 31, 2020. We outlined several case studies to demonstrate the model’s state- and local-level projection capabilities. We further demonstrated how model outcomes could be used to evaluate perceived levels of COVID-19 risk across different localities using a multi-criteria decision analysis framework. The model’s two, three, and four week out-of-sample projection errors varied on a state-by-state basis, and generally increased as the out-of-sample projection period was extended. Additionally, the prediction error in the state-level projections was generally due to an underestimation of cases and an overestimation of deaths. The proposed modeling approach can be used as a virtual laboratory to investigate a wide range of what-if scenarios and easily adapted to future high-consequence public health threats.
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spelling pubmed-82036602021-06-15 A multi-method approach to modeling COVID-19 disease dynamics in the United States Mokhtari, Amir Mineo, Cameron Kriseman, Jeffrey Kremer, Pedro Neal, Lauren Larson, John Sci Rep Article In this paper, we proposed a multi-method modeling approach to community-level spreading of COVID-19 disease. Our methodology was composed of interconnected age-stratified system dynamics models in an agent-based modeling framework that allowed for a granular examination of the scale and severity of disease spread, including metrics such as infection cases, deaths, hospitalizations, and ICU usage. Model parameters were calibrated using an optimization technique with an objective function to minimize error associated with the cumulative cases of COVID-19 during a training period between March 15 and October 31, 2020. We outlined several case studies to demonstrate the model’s state- and local-level projection capabilities. We further demonstrated how model outcomes could be used to evaluate perceived levels of COVID-19 risk across different localities using a multi-criteria decision analysis framework. The model’s two, three, and four week out-of-sample projection errors varied on a state-by-state basis, and generally increased as the out-of-sample projection period was extended. Additionally, the prediction error in the state-level projections was generally due to an underestimation of cases and an overestimation of deaths. The proposed modeling approach can be used as a virtual laboratory to investigate a wide range of what-if scenarios and easily adapted to future high-consequence public health threats. Nature Publishing Group UK 2021-06-14 /pmc/articles/PMC8203660/ /pubmed/34127757 http://dx.doi.org/10.1038/s41598-021-92000-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Mokhtari, Amir
Mineo, Cameron
Kriseman, Jeffrey
Kremer, Pedro
Neal, Lauren
Larson, John
A multi-method approach to modeling COVID-19 disease dynamics in the United States
title A multi-method approach to modeling COVID-19 disease dynamics in the United States
title_full A multi-method approach to modeling COVID-19 disease dynamics in the United States
title_fullStr A multi-method approach to modeling COVID-19 disease dynamics in the United States
title_full_unstemmed A multi-method approach to modeling COVID-19 disease dynamics in the United States
title_short A multi-method approach to modeling COVID-19 disease dynamics in the United States
title_sort multi-method approach to modeling covid-19 disease dynamics in the united states
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8203660/
https://www.ncbi.nlm.nih.gov/pubmed/34127757
http://dx.doi.org/10.1038/s41598-021-92000-w
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