Open Access

ARTICLE

Hospital Bed Allocation Strategy Based on Queuing Theory during the COVID-19 Epidemic

by Jing Hu¹, Gang Hu^2,*, Jiantao Cai³, Lipeng Xu², Qirun Wang⁴

1 Department of Rehabilitation Medicine, Children’s Hospital of Nanjing Medical University, Nanjing, 210008, China
2 College of Science and Engineering Management, Anhui University of Technology, Ma’anshan, 243002, China
3 Huai’an TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Huai’an, 223002, China
4 Department of Physics and Astronomy, University College London, London, 0044, UK

* Corresponding Author: Gang Hu. Email:

(This article belongs to the Special Issue: Artificial Intelligence and Information Technologies for COVID-19)

Computers, Materials & Continua 2021, 66(1), 793-803. https://doi.org/10.32604/cmc.2020.011110

Received 20 April 2020; Accepted 26 July 2020; Issue published 30 October 2020

Abstract

During the current epidemic, it is necessary to ensure the rehabilitation treatment of children with serious illness. At the same time, however, it is essential to effectively prevent cross-infection and prevent infections from occurring within the hospital setting. To resolve this contradiction, the rehabilitation department of Nanjing Children’s Hospital adjusted its bed allocation based on the queuing model, with reference to the regional source and classification of the children’s conditions in the rehabilitation department ward. The original triple rooms were transformed into a double room to enable the treatment of severely sick children coming from other places. A M/G/2 queuing model with priority was also applied to analyze the state of patient admissions. Moreover, patients in Nanjing were also classified into mild and severe cases. The M/M/1 queuing model with priority was used for analysis of this situation, so that severely ill children could be treated in time while patients with mild symptoms could be treated at home. This approach not only eases the bed tension in the ward, but also provides suitable conditions for controlling cross-infection.

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