SIS Epidemic Spreading with Heterogeneous Infection Rates

Research output: Contribution to journalArticleScientificpeer-review

27 Citations (Scopus)

Abstract

In this work, we aim to understand the influence of the heterogeneity of infection rates on the Susceptible-Infected-Susceptible (SIS) epidemic spreading. Employing the classic SIS model as the benchmark, we study the influence of the independently identically distributed infection rates on the average fraction of infected nodes in the metastable state. The log-normal, gamma and a newly designed distributions are considered for infection rates. We find that, when the recovery rate is small, i.e., the epidemic spreads out in both homogeneous and heterogeneous cases: 1) the heterogeneity of infection rates on average retards the virus spreading, and 2) a larger even-order moment of the infection rates leads to a smaller average fraction of infected nodes, but the odd-order moments contribute in the opposite way; when the recovery rate is large, i.e., the epidemic may die out or infect a small fraction of the population, the heterogeneity of infection rates may enhance the probability that the epidemic spreads out. Finally, we verify our conclusions via real-world networks with their heterogeneous infection rates. Our results suggest that, in reality the epidemic spread may not be so severe as the classic SIS model indicates, but to eliminate the epidemic is probably more difficult.

Original languageEnglish
Article number7937925
Pages (from-to)177-186
Number of pages10
JournalIEEE Transactions on Network Science and Engineering
Volume4
Issue number3
DOIs
Publication statusPublished - 2017

Keywords

  • epidemics
  • heterogeneous SIS model
  • infection rates
  • Networks
  • virus spreading

Fingerprint

Dive into the research topics of 'SIS Epidemic Spreading with Heterogeneous Infection Rates'. Together they form a unique fingerprint.

Cite this