Epidemic dynamics on social interaction networks

Mariem Jelassi; Kayode Oshinubi; Mustapha Rachdi; Jacques Demongeot; Mariem Jelassi; Kayode Oshinubi; Mustapha Rachdi; Jacques Demongeot

doi:10.3934/bioeng.2022025

AIMS Bioengineering

2022, Volume 9, Issue 4: 348-361. doi: 10.3934/bioeng.2022025

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Research article Special Issues

Epidemic dynamics on social interaction networks

1.
National School of Computer Sciences (ENSI), University La Manouba, 2010, La Manouba, Tunisia
2.
Laboratory AGEIS EA 7407, Team Tools for e-Gnosis Medical and Orange Labs Telecom4Health Labcom, Faculty of Medicine, University Grenoble Alpes (UGA), 38700 La Tronche, France

Received: 19 September 2022 Revised: 28 October 2022 Accepted: 07 November 2022 Published: 18 November 2022

The present paper aims to apply the mathematical ideas of the contagion networks in a discrete dynamic context to the modeling of two current pandemics, i.e., COVID-19 and obesity, that are identified as major risks by the World Health Organization. After providing a reminder of the main tools necessary to model epidemic propagation in a Boolean framework (Hopfield-type propagation equation, notion of centrality, existence of stationary states), we present two applications derived from the observation of real data and involving mathematical models for their interpretation. After a discussion of the obtained results of model simulations, multidisciplinary work perspectives (both on mathematical and biomedical sides) are proposed in order to increase the efficiency of the models currently used and improve both the comprehension of the contagion mechanism and the prediction of the dynamic behaviors of the pandemics' present and future states.
- boolean dynamic,
- epidemic modeling,
- graph centrality,
- COVID-19 pandemic,
- obesity pandemic
Citation: Mariem Jelassi, Kayode Oshinubi, Mustapha Rachdi, Jacques Demongeot. Epidemic dynamics on social interaction networks[J]. AIMS Bioengineering, 2022, 9(4): 348-361. doi: 10.3934/bioeng.2022025

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Abstract

The present paper aims to apply the mathematical ideas of the contagion networks in a discrete dynamic context to the modeling of two current pandemics, i.e., COVID-19 and obesity, that are identified as major risks by the World Health Organization. After providing a reminder of the main tools necessary to model epidemic propagation in a Boolean framework (Hopfield-type propagation equation, notion of centrality, existence of stationary states), we present two applications derived from the observation of real data and involving mathematical models for their interpretation. After a discussion of the obtained results of model simulations, multidisciplinary work perspectives (both on mathematical and biomedical sides) are proposed in order to increase the efficiency of the models currently used and improve both the comprehension of the contagion mechanism and the prediction of the dynamic behaviors of the pandemics' present and future states.

Acknowledgments

The authors would like to thank the Petroleum Technology Development Fund (PTDF) of Nigeria doctoral fellowship, in collaboration with the Campus France Africa Unit.

Conflict of interest

The authors declare no conflict of interest.

Author contributions

All authors have equally contributed to the investigation, computation and manuscript writing.

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