An Empirical Study on Patient Queuing after Medical Staff Supporting Disaster Areas in Northwest China

  • Wenxing Wang
  • Wenyuan Sun
Keywords: New Crown Epidemic Situation, ├ M┤|├ M┤|├ c┤|∞ Queuing Model, Waiting Time, SIR Model

Abstract

Recently, the new coronavirus has brought great disaster to human beings, so we have to take strong measures to suppress the large-scale outbreak of the disease. In this paper, by looking up the data of medical staff supporting Wuhan area in Northwest China, we build a queuing model of  to analyze the waiting time and staying time of patients. Secondly, due to the increase of patients, the burden of outpatient service is gradually increasing, which leads to the speed of epidemic spread greatly accelerated. Therefore,  model is constructed to analyze the relationship between patients and healers. The experimental results show that: (1) at the beginning of the data of more than 1000 medical staff, the patients were served for too long, which led to low efficiency. When they were supported, the efficiency was increasing with the increase of support, and the time was shortened, which was very helpful to relieve the medical pressure of outpatient. (2) With the increase of patients, at the same time, the number of healers is increasing, of course, there are also healthy people in it. At this time, we should focus on finding a suitable node, reducing the number of patients and increasing the number of healers, so as to effectively control the epidemic.

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References

Chu Yu, lingxu Chen, & Lihua Fu. Design of bank queuing system in the context of big data. Satellite TV and Broadband Multimedia, 15, 31-33.

Botao Zheng, Xiaowen Mao, Pingru Yang, Shi Xu, & Jianlu Hao. (2019). Calculation of elevator evacuation time in disaster based on queuing theory. Safety and Environmental Engineering, 26(01), 83-86 + 98.

Bo Yao, Yunbo Sun, Xinying Kong, Kai Gu, Xiaoyu Cheng, Qianqian Li, Qian Dong, & Jinyan Xing. (2020). Application of digital remote consultation system in improving precision medical treatment and avoiding cross infection of new coronavirus pneumonia. Journal of Precision Medicine, 35(01), 53-56.

Xinqiao Fu, Li Yuan, Jie Wan, liming Xiong, Han Zhang, & Menghan Hu. (2020). Application of DRGs principle in the evaluation of outpatient medical service ability. Modern Hospital, 20(03), 344-346.

Zongwen Ye. (2009). Application of M / M / C queuing model in barber service industry. Journal of Chongqing Normal University (Natural Science Edition), 26(02), 75-78.

Chaoqun Wang & Dechun Yuan. (2020). Research on the demand of large-scale civil aviation health evacuation aircraft based on queuing theory. Journal of Military Transportation College, 22(01), 19-23.

Wei Zhou & Qiang Wang. (2017). M / M / C model extension and application under dynamic input rate and service rate setting. Operations Research and Management, 26(02), 76-83.

Jiaqi Fu, Min Liu, Chunyan Deng, Juan Huang, Mingzhu Jiang, Qiang Guo, & Jianguo Liu. Covid-19 communication model under complex human flow network. Journal of University of Electronic Science and Technology.

Jiayi Qu, Qinyi Yao, Yunting Chen, & Zhigui Lin. (2019). Influencing factors of influenza transmission. Journal of Yangzhou University (Natural Science Edition), 22(03), 6-8.

Published
2020-06-30
How to Cite
Wenxing Wang, & Wenyuan Sun. (2020). An Empirical Study on Patient Queuing after Medical Staff Supporting Disaster Areas in Northwest China. International Journal of Engineering and Management Research, 10(3), 159-164. https://doi.org/10.31033/ijemr.10.3.24
Section
Articles