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COVID-19 Spatial Diffusion: A Markovian Agent-Based Model

Author

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  • Marco Gribaudo

    (Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy)

  • Mauro Iacono

    (Dipartimento Matematica e Fisica, Università degli Studi della Campania “Luigi Vanvitelli”, 81100 Caserta, Italy)

  • Daniele Manini

    (Dipartimento di Informatica, Università degli Studi di Torino, 10149 Torino, Italy)

Abstract

We applied a flexible modeling technique capable of representing dynamics of large populations interacting in space and time, namely Markovian Agents, to study the evolution of COVID-19 in Italy. Our purpose was to show that this modeling approach, that is based on mean field analysis models, provides good performances in describing the diffusion of phenomena, like COVID-19. The paper describes the application of this modeling approach to the Italian scenario and results are validated against real data available about the Italian official documentation of the diffusion of COVID-19. The model of each agent is organized similarly to what largely established in literature in the Susceptible-Infected-Recovered (SIR) family of approaches. Results match the main events taken by the Italian government and their effects.

Suggested Citation

  • Marco Gribaudo & Mauro Iacono & Daniele Manini, 2021. "COVID-19 Spatial Diffusion: A Markovian Agent-Based Model," Mathematics, MDPI, vol. 9(5), pages 1-12, February.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:5:p:485-:d:506699
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    References listed on IDEAS

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    1. Cleo Anastassopoulou & Lucia Russo & Athanasios Tsakris & Constantinos Siettos, 2020. "Data-based analysis, modelling and forecasting of the COVID-19 outbreak," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-21, March.
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    Cited by:

    1. Martin Kröger & Reinhard Schlickeiser, 2024. "On the Analytical Solution of the SIRV-Model for the Temporal Evolution of Epidemics for General Time-Dependent Recovery, Infection and Vaccination Rates," Mathematics, MDPI, vol. 12(2), pages 1-19, January.

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