Spreading of Infections on Network Models: Percolation Clusters and Random Trees

Hector Eduardo Roman and Fabrizio Croccolo
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Hector Eduardo Roman: Dipartimento di Fisica, Università di Milano-Bicocca, Piazza della Scienza 3, 20126 Milano, Italy
Fabrizio Croccolo: Laboratoire des Fluides Complexes et de leurs Réservoirs (LFCR), UMR 5150, Centre National de la Recherche Scientifique (CNRS), TOTAL, E2S UPPA, Universite de Pau et des Pays de l’Adour, 64600 Anglet, France

Mathematics, 2021, vol. 9, issue 23, 1-22

Abstract: We discuss network models as a general and suitable framework for describing the spreading of an infectious disease within a population. We discuss two types of finite random structures as building blocks of the network, one based on percolation concepts and the second one on random tree structures. We study, as is done for the SIR model, the time evolution of the number of susceptible (S), infected (I) and recovered (R) individuals, in the presence of a spreading infectious disease, by incorporating a healing mechanism for infecteds. In addition, we discuss in detail the implementation of lockdowns and how to simulate them. For percolation clusters, we present numerical results based on site percolation on a square lattice, while for random trees we derive new analytical results, which are illustrated in detail with a few examples. It is argued that such hierarchical networks can complement the well-known SIR model in most circumstances. We illustrate these ideas by revisiting USA COVID-19 data.

Keywords: spreading of infections; finite percolation clusters; random trees; lockdown effects (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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