Epidemic dynamics on social interaction networks

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.

Keywords

• boolean dynamic
• epidemic modeling
• graph centrality
• covid-19 pandemic
• obesity pandemic