IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v13y2022i2d10.1007_s13198-021-01312-7.html
   My bibliography  Save this article

Stochastic models on the transmission of novel COVID-19

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-021-01312-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s13198-021-01312-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mishra, Bimal Kumar & Keshri, Ajit Kumar & Saini, Dinesh Kumar & Ayesha, Syeda & Mishra, Binay Kumar & Rao, Yerra Shankar, 2021. "Mathematical model, forecast and analysis on the spread of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    2. Giovanni Dieguez & Cristiane Batistela & José R. C. Piqueira, 2023. "Controlling COVID-19 Spreading: A Three-Level Algorithm," Mathematics, MDPI, vol. 11(17), pages 1-39, September.
    3. 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).
    4. Çaparoğlu, Ömer Faruk & Ok, Yeşim & Tutam, Mahmut, 2021. "To restrict or not to restrict? Use of artificial neural network to evaluate the effectiveness of mitigation policies: A case study of Turkey," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    5. Erdoğan, Güneş & Yücel, Eda & Kiavash, Parinaz & Salman, F. Sibel, 2024. "Fair and effective vaccine allocation during a pandemic," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).
    6. František Božek & Irena Tušer, 2021. "Measures for Ensuring Sustainability during the Current Spreading of Coronaviruses in the Czech Republic," Sustainability, MDPI, vol. 13(12), pages 1-22, June.
    7. Singhal, Amit & Singh, Pushpendra & Lall, Brejesh & Joshi, Shiv Dutt, 2020. "Modeling and prediction of COVID-19 pandemic using Gaussian mixture model," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    8. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "Dynamic tracking with model-based forecasting for the spread of the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    9. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "A SIR model assumption for the spread of COVID-19 in different communities," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    10. Song, Jialu & Xie, Hujin & Gao, Bingbing & Zhong, Yongmin & Gu, Chengfan & Choi, Kup-Sze, 2021. "Maximum likelihood-based extended Kalman filter for COVID-19 prediction," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    11. Huang, Yubo & Wu, Yan & Zhang, Weidong, 2020. "Comprehensive identification and isolation policies have effectively suppressed the spread of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    12. Chakraborty, Tanujit & Ghosh, Indrajit, 2020. "Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    13. Gaetano Perone, 2020. "An ARIMA model to forecast the spread and the final size of COVID-2019 epidemic in Italy," Health, Econometrics and Data Group (HEDG) Working Papers 20/07, HEDG, c/o Department of Economics, University of York.
    14. Luca Bonacini & Giovanni Gallo & Fabrizio Patriarca, 2021. "Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 275-301, January.
    15. Christos A. Kontovas & Krishna Sooprayen, 2020. "Maritime Cargo Prioritisation during a Prolonged Pandemic Lockdown Using an Integrated TOPSIS-Knapsack Technique: A Case Study on Small Island Developing States—The Rodrigues Island," Sustainability, MDPI, vol. 12(19), pages 1-20, September.
    16. Memon, Zaibunnisa & Qureshi, Sania & Memon, Bisharat Rasool, 2021. "Assessing the role of quarantine and isolation as control strategies for COVID-19 outbreak: A case study," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    17. Amaral, Marco A. & Oliveira, Marcelo M. de & Javarone, Marco A., 2021. "An epidemiological model with voluntary quarantine strategies governed by evolutionary game dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    18. Jahanshahi, Hadi & Munoz-Pacheco, Jesus M. & Bekiros, Stelios & Alotaibi, Naif D., 2021. "A fractional-order SIRD model with time-dependent memory indexes for encompassing the multi-fractional characteristics of the COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    19. Salgotra, Rohit & Gandomi, Mostafa & Gandomi, Amir H., 2020. "Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    20. Ghosh, Mousam & Ghosh, Swarnankur & Ghosh, Suman & Panda, Goutam Kumar & Saha, Pradip Kumar, 2021. "Dynamic model of infected population due to spreading of pandemic COVID-19 considering both intra and inter zone mobilization factors with rate of detection," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ijsaem:v:13:y:2022:i:2:d:10.1007_s13198-021-01312-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.