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Mathematical Modelling to Predict the Effect of Vaccination on Delay and Rise of COVID-19 Cases Management

Author

Listed:
  • Charu Arora

    (Department of Applied Sciences, Bharati Vidyapeeth’s College of Engineering, Delhi 110063, India
    These authors contributed equally to this work.)

  • Poras Khetarpal

    (Department of Information Technology, Bharati Vidyapeeth’s College of Engineering, Delhi 110063, India
    These authors contributed equally to this work.)

  • Saket Gupta

    (Department of Instrumentation and Control Engineering, Bharati Vidyapeeth’s College of Engineering, Delhi 110063, India
    These authors contributed equally to this work.)

  • Nuzhat Fatema

    (Faculty of Business and Management, Universiti Sultan Zainal Abidin (UniSZA), Gong Badak, Kuala Terengganu 21300, Terengganu, Malaysia
    Intelligent Prognostic Private Limited, Delhi 110093, India
    These authors contributed equally to this work.)

  • Hasmat Malik

    (Department of Electrical Power Engineering, Faculty of Electrical Engineering, University Technology Malaysia (UTM), Johor Bahru 81310, Johor, Malaysia
    These authors contributed equally to this work.)

  • Asyraf Afthanorhan

    (Faculty of Business and Management, Universiti Sultan Zainal Abidin (UniSZA), Gong Badak, Kuala Terengganu 21300, Terengganu, Malaysia
    These authors contributed equally to this work.)

Abstract

In this paper, a mathematical model based on COVID-19 is developed to study and manage disease outbreaks. The effect of vaccination with regard to its efficacy and percentage of population vaccinated in a closed population is investigated. To study virus transmission, the system employs six nonlinear ordinary differential equations with susceptible–exposed–asymptomatic–infected–vaccinated–recovered populations and the basic reproduction number are calculated. The proposed model describes for highly infectious diseases (such as COVID-19) in a closed containment area with no migration. This paper considers that the percentage of vaccinated population has a significant impact on the number of COVID-19 positive cases during the pandemic wave and examines how the pandemic rise time is delayed. Numerical simulation to investigate disease outbreaks when the community is undergoing vaccination is performed, taking the efficacy rate of the vaccine into account. Sensitivity Index values are calculated for the reproduction number and their relations with few other parameters are depicted.

Suggested Citation

  • Charu Arora & Poras Khetarpal & Saket Gupta & Nuzhat Fatema & Hasmat Malik & Asyraf Afthanorhan, 2023. "Mathematical Modelling to Predict the Effect of Vaccination on Delay and Rise of COVID-19 Cases Management," Mathematics, MDPI, vol. 11(4), pages 1-15, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:821-:d:1059238
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    References listed on IDEAS

    as
    1. Yousefpour, Amin & Jahanshahi, Hadi & Bekiros, Stelios, 2020. "Optimal policies for control of the novel coronavirus disease (COVID-19) outbreak," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    2. Singh, Sarbjit & Parmar, Kulwinder Singh & Kumar, Jatinder & Makkhan, Sidhu Jitendra Singh, 2020. "Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
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