Optimal curing policy for epidemic spreading over a community network with heterogeneous population

Stefania Ottaviano, Francesco De Pellegrini, Stefano Bonaccorsi, Piet Van Mieghem

Research output: Contribution to journalArticleScientificpeer-review

10 Citations (Scopus)

Abstract

The design of an efficient curing policy, able to stem an epidemic process at an affordable cost, has to account for the structure of the population contact network supporting the contagious process. Thus, we tackle the problem of allocating recovery resources among the population, at the lowest cost possible to prevent the epidemic from persisting indefinitely in the network. Specifically, we analyse a susceptible- infected-susceptible epidemic process spreading over a weighted graph, by means of a first-order meanfield approximation. First,wedescribe the influence of the contact network on the dynamics of the epidemics among a heterogeneous population, that is possibly divided into communities. For the case of a community network, our investigation relies on the graph-theoretical notion of equitable partition; we show that the epidemic threshold, a key measure of the network robustness against epidemic spreading, can be determined using a lower-dimensional dynamical system. Exploiting the computation of the epidemic threshold, we determine a cost-optimal curing policy by solving a convex minimization problem, which possesses a reduced dimension in the case of a community network. Lastly, we consider a two-level optimal curing problem, for which an algorithm is designed with a polynomial time complexity in the network size.

Original languageEnglish
Pages (from-to)800-829
Number of pages30
JournalJournal of Complex Networks
Volume6
Issue number5
DOIs
Publication statusPublished - 2018

Keywords

  • Community network
  • Convex optimization
  • Equitable partitions
  • Graph spectra
  • Heterogeneous SIS model

Fingerprint

Dive into the research topics of 'Optimal curing policy for epidemic spreading over a community network with heterogeneous population'. Together they form a unique fingerprint.

Cite this