Mathematical Models for COVID-19 Pandemic: A Comparative Analysis

Year : 2024 | Volume : 01 | Issue : 01 | Page : 42 53
    By

    Narendra J. Padole,

  • Vinod Ramteke,

  • Manish L. Jivtode,

  1. Assitant Professor, P. G. Department of Computer Science and Technolocy, Degree College of Physical Education, Shree H.V.P. Mandal’s Amravati, Santa Gadge Baba University, Amravati,, Maharashtra, India
  2. Associate Professor, Department of Computer Science, Janta Mahavidyalya, Chandrapur, Godwana University , Gadchiroli, Maharashtra, India
  3. Assitant Professor, Department of Computer Science, Janta Mahavidyalya, Chandrapur, Godwana University , Gadchiroli, Maharashtra, India

Abstract

The COVID-19 pandemic has really underlined the importance of mathematical modeling in understanding disease-spread dynamics and especially informing public health interventions. The paper aims to provide a comprehensive comparative analysis of various mathematical models used for COVID-19 studies, with a focus on assumptions underlying those models, strengths, and also the limitations in their applications as well as special focus is given to compartmental models, agent-based models, machine learning-enhanced models, and hybrid approaches. The insights developed from this analysis can inform future pandemic modeling and policy-making efforts.

Keywords: Modeling for COVID-19, mathematical models, compartmental models, agent-based models, machine learning, hybrid models, policy applications

[This article belongs to Recent Trends in Mathematics ]

How to cite this article:
Narendra J. Padole, Vinod Ramteke, Manish L. Jivtode. Mathematical Models for COVID-19 Pandemic: A Comparative Analysis. Recent Trends in Mathematics. 2025; 01(01):42-53.
How to cite this URL:
Narendra J. Padole, Vinod Ramteke, Manish L. Jivtode. Mathematical Models for COVID-19 Pandemic: A Comparative Analysis. Recent Trends in Mathematics. 2025; 01(01):42-53. Available from: https://journals.stmjournals.com/rtm/article=2025/view=223217


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Regular Issue Subscription Review Article
Volume 01
Issue 01
Received 08/02/2025
Accepted 18/06/2025
Published 30/06/2025
Publication Time 142 Days
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