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Equation-Based Modeling vs. Agent-Based Modeling with Applications to the Spread of COVID-19 Outbreak

Author

Listed:
  • Selain K. Kasereka

    (Mathematics, Statistics and Computer Science Department, University of Kinshasa, Kinshasa, Congo
    Artificial Intelligence, Big Data and Modeling Simulation Research Center (ABIL), Kinshasa, Congo)

  • Glody N. Zohinga

    (Mathematics, Statistics and Computer Science Department, University of Kinshasa, Kinshasa, Congo
    Artificial Intelligence, Big Data and Modeling Simulation Research Center (ABIL), Kinshasa, Congo)

  • Vogel M. Kiketa

    (Business English and Computer Science Department, University of Kinshasa, Kinshasa, Congo)

  • Ruffin-Benoît M. Ngoie

    (Artificial Intelligence, Big Data and Modeling Simulation Research Center (ABIL), Kinshasa, Congo
    Department of Mathematics, Institut Supérieur Pédagogique de Mbanza-Ngungu, Mbanza-Ngungu, Congo)

  • Eddy K. Mputu

    (Artificial Intelligence, Big Data and Modeling Simulation Research Center (ABIL), Kinshasa, Congo
    Department of Mathematics, Institut Supérieur Pédagogique de Mbanza-Ngungu, Mbanza-Ngungu, Congo)

  • Nathanaël M. Kasoro

    (Mathematics, Statistics and Computer Science Department, University of Kinshasa, Kinshasa, Congo
    Artificial Intelligence, Big Data and Modeling Simulation Research Center (ABIL), Kinshasa, Congo)

  • Kyamakya Kyandoghere

    (Institute of Smart Systems Technologies, University of Klagenfurt, 9020 Klagenfurt, Austria)

Abstract

In this paper, we explore two modeling approaches to understanding the dynamics of infectious diseases in the population: equation-based modeling (EBM) and agent-based modeling (ABM). To achieve this, a comparative study of these approaches was conducted and we highlighted their advantages and disadvantages. Two case studies on the spread of the COVID-19 pandemic were carried out using both approaches. The results obtained show that differential equation-based models are faster but still simplistic, while agent-based models require more machine capabilities but are more realistic and very close to biology. Based on these outputs, it seems that the coupling of both approaches could be an interesting compromise.

Suggested Citation

  • Selain K. Kasereka & Glody N. Zohinga & Vogel M. Kiketa & Ruffin-Benoît M. Ngoie & Eddy K. Mputu & Nathanaël M. Kasoro & Kyamakya Kyandoghere, 2023. "Equation-Based Modeling vs. Agent-Based Modeling with Applications to the Spread of COVID-19 Outbreak," Mathematics, MDPI, vol. 11(1), pages 1-21, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:1:p:253-:d:1024009
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    Citations

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    Cited by:

    1. Alexandru Topîrceanu, 2023. "On the Impact of Quarantine Policies and Recurrence Rate in Epidemic Spreading Using a Spatial Agent-Based Model," Mathematics, MDPI, vol. 11(6), pages 1-19, March.
    2. Eduarda Asfora Frej & Lucia Reis Peixoto Roselli & Alexandre Ramalho Alberti & Murilo Amorim Britto & Evônio de Barros Campelo Júnior & Rodrigo José Pires Ferreira & Adiel Teixeira de Almeida, 2023. "Collaborative Decision Model for Allocating Intensive Care Units Beds with Scarce Resources in Health Systems: A Portfolio Based Approach under Expected Utility Theory and Bayesian Decision Analysis," Mathematics, MDPI, vol. 11(3), pages 1-15, January.

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