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The Critical Role of Networks to Describe Disease Spreading Dynamics in Social Systems: A Perspective

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  • Michele Bellingeri

    (Dipartimento di Scienze Matematiche, Fisiche e Informatiche, Università di Parma, Parco Area delle Scienze, 7/A, 43124 Parma, Italy
    Istituto Nazione di Fisica Nucleare (INFN), Gruppo Collegato di Parma, Parco Area delle Scienze, 7/A, 43124 Parma, Italy)

  • Daniele Bevacqua

    (PSH, UR 1115, INRAE, Domaine Saint-Paul, 228 Route de l’Aérodrome, 84914 Avignon, France)

  • Francesco Scotognella

    (Dipartimento di Scienza Applicata e Tecnologia (DISAT), Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, Italy)

  • Davide Cassi

    (Dipartimento di Scienze Matematiche, Fisiche e Informatiche, Università di Parma, Parco Area delle Scienze, 7/A, 43124 Parma, Italy
    Istituto Nazione di Fisica Nucleare (INFN), Gruppo Collegato di Parma, Parco Area delle Scienze, 7/A, 43124 Parma, Italy)

Abstract

This review underscores the critical significance of incorporating networks science in epidemiology. Classic mathematical compartmental models (CMs) employed to describe epidemic spreading may fail to capture the intricacies of real disease dynamics. Rooted in the mean-field assumption, CMs oversimplify by assuming that every individual has the potential to “infect” any other, neglecting the inherent complexity of underlying network structures. Since social interactions follow a networked pattern with specific links between individuals based on social behaviors, joining classic CMs and network science in epidemiology becomes essential for a more authentic portrayal of epidemic spreading. This review delves into noteworthy research studies that, from various perspectives, elucidate how the synergy between networks and CMs can enhance the accuracy of epidemic descriptions. In conclusion, we explore research prospects aimed at further elevating the integration of networks within the realm of epidemiology, recognizing its pivotal role in refining our understanding of disease dynamics.

Suggested Citation

  • Michele Bellingeri & Daniele Bevacqua & Francesco Scotognella & Davide Cassi, 2024. "The Critical Role of Networks to Describe Disease Spreading Dynamics in Social Systems: A Perspective," Mathematics, MDPI, vol. 12(6), pages 1-11, March.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:6:p:792-:d:1353361
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    References listed on IDEAS

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    3. Marcel Salathé & James H Jones, 2010. "Dynamics and Control of Diseases in Networks with Community Structure," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-11, April.
    4. Katherine Klise & Walt Beyeler & Patrick Finley & Monear Makvandi, 2021. "Analysis of mobility data to build contact networks for COVID-19," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-18, April.
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