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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.
Afiliación
  • Xinyu Li; Department of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South University,
  • Yufeng Cai; Department of Biomedical Informatics, School of Life Sciences, Central South University, Changsha, Hunan
  • Yinghe Ding; Xiangya School of Medicine, Central South University
  • Jia-da Li; Department of Biomedical Informatics, School of Life Sciences, Central South University
  • Guoqing Huang; Department of Emergency, Xiangya Hospital, Central South University
  • Ye Liang; Department of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South University,
  • Linyong Xu; Department of Biomedical Informatics, School of Life Sciences, Central South University
Preprint en En | PREPRINT-MEDRXIV | ID: ppmedrxiv-20152330
ABSTRACT
The outbreak of COVID-19 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. We 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 cure time, and (4) population mobility. Finally, a method for predicting the reasonable number of isolation beds was summarized through multiple linear regression. The prediction equation can be easily and quickly applied to estimate the demanded number of isolation beds in a COVID-19-affected city. A detailed explanation is given for the specific measurement of each parameter in the article.
Licencia
cc_by_nc_nd
Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-MEDRXIV Tipo de estudio: Prognostic_studies Idioma: En Año: 2020 Tipo del documento: Preprint
Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-MEDRXIV Tipo de estudio: Prognostic_studies Idioma: En Año: 2020 Tipo del documento: Preprint