A model to rate strategies for managing disease due to COVID-19 infection

Abstract Considering looming fatality and economic recession, effective policy making based on ongoing COVID-19 pandemic is an urgent and standing issue. Numerous issues for controlling infection have arisen from public discussion led by medical professionals. Yet understanding of these factors has...

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Autores principales:	Shiyan Wang, Doraiswami Ramkrishna
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/da1b9e03eb8741db92e849b66995be84
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spelling	oai:doaj.org-article:da1b9e03eb8741db92e849b66995be842021-12-02T15:12:41ZA model to rate strategies for managing disease due to COVID-19 infection10.1038/s41598-020-79817-72045-2322https://doaj.org/article/da1b9e03eb8741db92e849b66995be842020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79817-7https://doaj.org/toc/2045-2322Abstract Considering looming fatality and economic recession, effective policy making based on ongoing COVID-19 pandemic is an urgent and standing issue. Numerous issues for controlling infection have arisen from public discussion led by medical professionals. Yet understanding of these factors has been necessarily qualitative and control measures to correct unfavorable trends specific to an infection area have been lacking. The logical implement for control is a large scale stochastic model with countless parameters lacking robustness and requiring enormous data. This paper presents a remedy for this vexing problem by proposing an alternative approach. Machine learning has come to play a widely circulated role in the study of complex data in recent times. We demonstrate that when machine learning is employed together with the mechanistic framework of a mathematical model, there can be a considerably enhanced understanding of complex systems. A mathematical model describing the viral infection dynamics reveals two transmissibility parameters influenced by the management strategies in the area for the control of the current pandemic. Both parameters readily yield the peak infection rate and means for flattening the curve, which is correlated to different management strategies by employing machine learning, enabling comparison of different strategies and suggesting timely alterations. Treatment of population data with the model shows that restricted non-essential business closure, school closing and strictures on mass gathering influence the spread of infection. While a rational strategy for initiation of an economic reboot would call for a wider perspective of the local economics, the model can speculate on its timing based on the status of the infection as reflected by its potential for an unacceptably renewed viral onslaught.Shiyan WangDoraiswami RamkrishnaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shiyan Wang Doraiswami Ramkrishna A model to rate strategies for managing disease due to COVID-19 infection
description	Abstract Considering looming fatality and economic recession, effective policy making based on ongoing COVID-19 pandemic is an urgent and standing issue. Numerous issues for controlling infection have arisen from public discussion led by medical professionals. Yet understanding of these factors has been necessarily qualitative and control measures to correct unfavorable trends specific to an infection area have been lacking. The logical implement for control is a large scale stochastic model with countless parameters lacking robustness and requiring enormous data. This paper presents a remedy for this vexing problem by proposing an alternative approach. Machine learning has come to play a widely circulated role in the study of complex data in recent times. We demonstrate that when machine learning is employed together with the mechanistic framework of a mathematical model, there can be a considerably enhanced understanding of complex systems. A mathematical model describing the viral infection dynamics reveals two transmissibility parameters influenced by the management strategies in the area for the control of the current pandemic. Both parameters readily yield the peak infection rate and means for flattening the curve, which is correlated to different management strategies by employing machine learning, enabling comparison of different strategies and suggesting timely alterations. Treatment of population data with the model shows that restricted non-essential business closure, school closing and strictures on mass gathering influence the spread of infection. While a rational strategy for initiation of an economic reboot would call for a wider perspective of the local economics, the model can speculate on its timing based on the status of the infection as reflected by its potential for an unacceptably renewed viral onslaught.
format	article
author	Shiyan Wang Doraiswami Ramkrishna
author_facet	Shiyan Wang Doraiswami Ramkrishna
author_sort	Shiyan Wang
title	A model to rate strategies for managing disease due to COVID-19 infection
title_short	A model to rate strategies for managing disease due to COVID-19 infection
title_full	A model to rate strategies for managing disease due to COVID-19 infection
title_fullStr	A model to rate strategies for managing disease due to COVID-19 infection
title_full_unstemmed	A model to rate strategies for managing disease due to COVID-19 infection
title_sort	model to rate strategies for managing disease due to covid-19 infection
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/da1b9e03eb8741db92e849b66995be84
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A model to rate strategies for managing disease due to COVID-19 infection

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