Susceptible-Infected-Removed Mathematical Model under Deep Learning in Hospital Infection Control of Novel Coronavirus Pneumonia

Objective. This research aimed to explore the application of a mathematical model based on deep learning in hospital infection control of novel coronavirus (COVID-19) pneumonia. Methods. First, the epidemic data of Beijing, China, were utilized to make a definite susceptible-infected-removed (SIR) m...

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Autores principales:	Ting Liu, Yanling Bai, Mingmei Du, Yueming Gao, Yunxi Liu
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Medicine (General) R5-920 Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/da9e74e26b77452297bc13226ec95430
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spelling	oai:doaj.org-article:da9e74e26b77452297bc13226ec954302021-11-08T02:36:50ZSusceptible-Infected-Removed Mathematical Model under Deep Learning in Hospital Infection Control of Novel Coronavirus Pneumonia2040-230910.1155/2021/1535046https://doaj.org/article/da9e74e26b77452297bc13226ec954302021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1535046https://doaj.org/toc/2040-2309Objective. This research aimed to explore the application of a mathematical model based on deep learning in hospital infection control of novel coronavirus (COVID-19) pneumonia. Methods. First, the epidemic data of Beijing, China, were utilized to make a definite susceptible-infected-removed (SIR) model fitting to determine the estimated value of the COVID-19 removal intensity β, which was then used to do a determined SIR model and a stochastic SIR model fitting for the hospital. In addition, the reasonable β and γ estimates of the hospital were determined, and the spread of the epidemic in hospital was simulated, to discuss the impact of basal reproductive number changes, isolation, vaccination, and so forth on COVID-19. Results. There was a certain gap between the fitting of SIR to the remover and the actual data. The fitting of the number of infections was accurate. The growth rate of the number of infections decreased after measures, such as isolation, were taken. The effect of herd immunity was achieved after the overall immunity reached 70.9%. Conclusion. The SIR model based on deep learning and the stochastic SIR fitting model were accurate in judging the development trend of the epidemic, which can provide basis and reference for hospital epidemic infection control.Ting LiuYanling BaiMingmei DuYueming GaoYunxi LiuHindawi LimitedarticleMedicine (General)R5-920Medical technologyR855-855.5ENJournal of Healthcare Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine (General) R5-920 Medical technology R855-855.5
spellingShingle	Medicine (General) R5-920 Medical technology R855-855.5 Ting Liu Yanling Bai Mingmei Du Yueming Gao Yunxi Liu Susceptible-Infected-Removed Mathematical Model under Deep Learning in Hospital Infection Control of Novel Coronavirus Pneumonia
description	Objective. This research aimed to explore the application of a mathematical model based on deep learning in hospital infection control of novel coronavirus (COVID-19) pneumonia. Methods. First, the epidemic data of Beijing, China, were utilized to make a definite susceptible-infected-removed (SIR) model fitting to determine the estimated value of the COVID-19 removal intensity β, which was then used to do a determined SIR model and a stochastic SIR model fitting for the hospital. In addition, the reasonable β and γ estimates of the hospital were determined, and the spread of the epidemic in hospital was simulated, to discuss the impact of basal reproductive number changes, isolation, vaccination, and so forth on COVID-19. Results. There was a certain gap between the fitting of SIR to the remover and the actual data. The fitting of the number of infections was accurate. The growth rate of the number of infections decreased after measures, such as isolation, were taken. The effect of herd immunity was achieved after the overall immunity reached 70.9%. Conclusion. The SIR model based on deep learning and the stochastic SIR fitting model were accurate in judging the development trend of the epidemic, which can provide basis and reference for hospital epidemic infection control.
format	article
author	Ting Liu Yanling Bai Mingmei Du Yueming Gao Yunxi Liu
author_facet	Ting Liu Yanling Bai Mingmei Du Yueming Gao Yunxi Liu
author_sort	Ting Liu
title	Susceptible-Infected-Removed Mathematical Model under Deep Learning in Hospital Infection Control of Novel Coronavirus Pneumonia
title_short	Susceptible-Infected-Removed Mathematical Model under Deep Learning in Hospital Infection Control of Novel Coronavirus Pneumonia
title_full	Susceptible-Infected-Removed Mathematical Model under Deep Learning in Hospital Infection Control of Novel Coronavirus Pneumonia
title_fullStr	Susceptible-Infected-Removed Mathematical Model under Deep Learning in Hospital Infection Control of Novel Coronavirus Pneumonia
title_full_unstemmed	Susceptible-Infected-Removed Mathematical Model under Deep Learning in Hospital Infection Control of Novel Coronavirus Pneumonia
title_sort	susceptible-infected-removed mathematical model under deep learning in hospital infection control of novel coronavirus pneumonia
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/da9e74e26b77452297bc13226ec95430
work_keys_str_mv	AT tingliu susceptibleinfectedremovedmathematicalmodelunderdeeplearninginhospitalinfectioncontrolofnovelcoronaviruspneumonia AT yanlingbai susceptibleinfectedremovedmathematicalmodelunderdeeplearninginhospitalinfectioncontrolofnovelcoronaviruspneumonia AT mingmeidu susceptibleinfectedremovedmathematicalmodelunderdeeplearninginhospitalinfectioncontrolofnovelcoronaviruspneumonia AT yueminggao susceptibleinfectedremovedmathematicalmodelunderdeeplearninginhospitalinfectioncontrolofnovelcoronaviruspneumonia AT yunxiliu susceptibleinfectedremovedmathematicalmodelunderdeeplearninginhospitalinfectioncontrolofnovelcoronaviruspneumonia
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Susceptible-Infected-Removed Mathematical Model under Deep Learning in Hospital Infection Control of Novel Coronavirus Pneumonia

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