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Stochastic models on the transmission of novel COVID-19

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  • Bimal Kumar Mishra

    (Markham College of Commerce)

Abstract

New diseases have always been part of humanity’s world, and some of them had created severe threat to human kind and challenge to the researchers and medical practitioners. The deadly novel coronavirus SARS-CoV-2 (severe acute respiratory syndrome- coronavirus -2) said to be COVID-19, the name given by WHO on February 11, 2020, is presently the most disastrous infectious disease. In the present paper our basic objective is to assess the risk of spreading the disease in human population and is measured in terms of probability. The proposed stochastic models help us to understand the probability of infection to n number of customers when these customers have spent time t in any system, say, shopping mall or public transportation or restaurant. Stochastic models are developed with arrival rate of the customers towards the system to be considered as a Poisson distribution and service time following an exponential distribution. A special case of cardiac centre is considered in this paper, where the risk of COVID-19 is highly contagion, with limited number of beds and doctors.

Suggested Citation

  • Bimal Kumar Mishra, 2022. "Stochastic models on the transmission of novel COVID-19," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 599-603, April.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:2:d:10.1007_s13198-021-01312-7
    DOI: 10.1007/s13198-021-01312-7
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    References listed on IDEAS

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    1. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    2. Olivares, Alberto & Staffetti, Ernesto, 2021. "Uncertainty quantification of a mathematical model of COVID-19 transmission dynamics with mass vaccination strategy," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    3. Mishra, Bimal Kumar & Keshri, Ajit Kumar & Rao, Yerra Shankar & Mishra, Binay Kumar & Mahato, Buddhadeo & Ayesha, Syeda & Rukhaiyyar, Bansidhar Prasad & Saini, Dinesh Kumar & Singh, Aditya Kumar, 2020. "COVID-19 created chaos across the globe: Three novel quarantine epidemic models," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    4. Batistela, Cristiane M. & Correa, Diego P.F. & Bueno, Átila M & Piqueira, José Roberto C., 2021. "SIRSi compartmental model for COVID-19 pandemic with immunity loss," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    5. Tagliazucchi, E. & Balenzuela, P. & Travizano, M. & Mindlin, G.B. & Mininni, P.D., 2020. "Lessons from being challenged by COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
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