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Prescriptive Analytics-Based SIRM Model for Predicting Covid-19 Outbreak

Author

Listed:
  • Jamal Al Qundus

    (Middle East University)

  • Shivam Gupta

    (NEOMA Business School)

  • Hesham Abusaimeh

    (Middle East University)

  • Silvio Peikert

    (Fraunhofer, FOKUS)

  • Adrian Paschke

    (Fraunhofer, FOKUS)

Abstract

Predicting the outbreak of a pandemic is an important measure in order to help saving people lives threatened by Covid-19. Having information about the possible spread of the pandemic, authorities and people can make better decisions. For example, such analyses help developing better strategies for distributing vaccines and medicines. This paper has modified the original Susceptible-Infectious-Recovered (SIR) model to Susceptible-Immune-Infected-Recovered (SIRM) which includes the Immunity ratio as a parameter to enhance the prediction of the pandemic. SIR is a widely used model to predict the spread of a pandemic. Many types of pandemics imply many variants of the SIR models which make it very difficult to find out the best model that matches the running pandemic. The simulation of this paper used the published data about the spread of the pandemic in order to examine our new SIRM. The results showed clearly that our new SIRM covering the aspects of vaccine and medicine is an appropriate model to predict the behavior of the pandemic.

Suggested Citation

  • Jamal Al Qundus & Shivam Gupta & Hesham Abusaimeh & Silvio Peikert & Adrian Paschke, 2023. "Prescriptive Analytics-Based SIRM Model for Predicting Covid-19 Outbreak," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(2), pages 235-246, June.
    • Handle: RePEc:spr:gjofsm:v:24:y:2023:i:2:d:10.1007_s40171-023-00337-0
    DOI: 10.1007/s40171-023-00337-0
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    1. Samia Zaoui & Clovis Foguem & Dieudonné Tchuente & Samuel Fosso-Wamba & Bernard Kamsu-Foguem, 2023. "The Viability of Supply Chains with Interpretable Learning Systems: The Case of COVID-19 Vaccine Deliveries," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(4), pages 633-657, December.

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