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

Abstract 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 se...

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Autores principales:	Amir Mokhtari, Cameron Mineo, Jeffrey Kriseman, Pedro Kremer, Lauren Neal, John Larson
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/508d5bc1527646498fbe55f99bde001e
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spelling	oai:doaj.org-article:508d5bc1527646498fbe55f99bde001e2021-12-02T17:41:10ZA multi-method approach to modeling COVID-19 disease dynamics in the United States10.1038/s41598-021-92000-w2045-2322https://doaj.org/article/508d5bc1527646498fbe55f99bde001e2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92000-whttps://doaj.org/toc/2045-2322Abstract 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.Amir MokhtariCameron MineoJeffrey KrisemanPedro KremerLauren NealJohn LarsonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Amir Mokhtari Cameron Mineo Jeffrey Kriseman Pedro Kremer Lauren Neal John Larson A multi-method approach to modeling COVID-19 disease dynamics in the United States
description	Abstract 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.
format	article
author	Amir Mokhtari Cameron Mineo Jeffrey Kriseman Pedro Kremer Lauren Neal John Larson
author_facet	Amir Mokhtari Cameron Mineo Jeffrey Kriseman Pedro Kremer Lauren Neal John Larson
author_sort	Amir Mokhtari
title	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_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_sort	multi-method approach to modeling covid-19 disease dynamics in the united states
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/508d5bc1527646498fbe55f99bde001e
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A multi-method approach to modeling COVID-19 disease dynamics in the United States

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