Modelling and performance analysis of the COVID-19 emergency collaborative process based on a stochastic Petri net

Qinying Sun and Haiqun Ma

Reliability Engineering and System Safety, 2024, vol. 241, issue C

Abstract: The prevention and control of COVID-19 requires coordination amongst various departments, including health care and disease control institutions, transportation departments, hospitals, public security emergency response departments, and community management departments. The efficiency of emergency coordination directly affects the effectiveness of pandemic prevention and control, and effective emergency coordination is the key to avoiding the worsening and spread of COVID-19. However, due to the uncertainty of rescue time prediction, the performance analysis of and research on the emergency collaborative process for COVID-19 are not sufficient. Considering the interrelationship of different stages of emergency coordination, this study establishes a stochastic Petri net model. Then, it sets up an isomorphic Markov chain based on the SPN model, and obtains the linear equation of the probability of stability. Finally, taking the COVID-19 outbreak in Xi'an as an example, this study conducts a static simulation analysis of COVID-19 prevention and control emergency collaborative action. By calculating the transition utilization rate and the busy probability of each place, the key positions and transition positions in the process of emergency coordination are identified, and the key stages and action lists of COVID-19 prevention and control emergency coordination are generated. On this basis, this paper proposes countermeasures and suggestions for the targeted prevention and control of COVID-19.

Keywords: Emergency collaboration; Stochastic Petri net; Pandemic prevention and control; COVID-19 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:241:y:2024:i:c:s0951832023005458

DOI: 10.1016/j.ress.2023.109631

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