Evidence map›Paper›PMID 34221726›Full record

ArticlePeerJ2021

Discrete simulation analysis of COVID-19 and prediction of isolation bed numbers.

Xinyu Li, Yufeng Cai, Yinghe Ding, Jia-Da Li, Guoqing Huang, Ye Liang, Linyong Xu

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In one paragraph

Article in PeerJ, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xinyu Li *Department of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South University, Changsha, China.
Yufeng Cai *Department of Biomedical Informatics, School of Life Sciences, Central South University, Changsha, China.ORCID 0000-0002-6499-2710
Yinghe DingXiangya School of Medicine, Central South University, Changsha, China.ORCID 0000-0002-5316-2535
Jia-Da LiDepartment of Biomedical Informatics, School of Life Sciences, Central South University, Changsha, China.
Guoqing HuangDepartment of Emergency, Xiangya Hospital, Central South University, Changsha, China.
Ye LiangDepartment of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South University, Changsha, China.
Linyong XuDepartment of Biomedical Informatics, School of Life Sciences, Central South University, Changsha, China.ORCID 0000-0001-5130-2461

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe outbreak of COVID-19 has been defined by the World Health Organization as a pandemic, and containment depends on traditional public health measures. However, the explosive growth of the number of infected cases in a short period of time has caused tremendous pressure on medical systems. Adequate isolation facilities are essential to control outbreaks, so this study aims to quickly estimate the demand and number of isolation beds.

methodsWe established a discrete simulation model for epidemiology. By adjusting or fitting necessary epidemic parameters, the effects of the following indicators on the development of the epidemic and the occupation of medical resources were explained: (1) incubation period, (2) response speed and detection capacity of the hospital, (3) disease healing time, and (4) population mobility. Finally, a method for predicting the number of isolation beds was summarized through multiple linear regression. This is a city level model that simulates the epidemic situation from the perspective of population mobility.

resultsThrough simulation, we show that the incubation period, response speed and detection capacity of the hospital, disease healing time, degree of population mobility, and infectivity of cured patients have different effects on the infectivity, scale, and duration of the epidemic. Among them, (1) incubation period, (2) response speed and detection capacity of the hospital, (3) disease healing time, and (4) population mobility have a significant impact on the demand and number of isolation beds (

Indexed as

Computer simulationCOVID-19Modeling and AnalysisNumber of isolation beds

Identifiers

PMID34221726
PMCPMC8234972

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.