Multi-state epidemic processes on complex networks

Naoki Masuda, Norio Konno

Research output: Contribution to journalArticlepeer-review

63 Citations (Scopus)

Abstract

Infectious diseases are practically represented by models with multiple states and complex transition rules corresponding to, for example, birth, death, infection, recovery, disease progression, and quarantine. In addition, networks underlying infection events are often much more complex than described by meanfield equations or regular lattices. In models with simple transition rules such as the SIS and SIR models, heterogeneous contact rates are known to decrease epidemic thresholds. We analyse steady states of various multi-state disease propagation models with heterogeneous contact rates. In many models, heterogeneity simply decreases epidemic thresholds. However, in models with competing pathogens and mutation, coexistence of different pathogens for small infection rates requires network-independent conditions in addition to heterogeneity in contact rates. Furthermore, models without spontaneous neighbor-independent state transitions, such as cyclically competing species, do not show heterogeneity effects.

Original languageEnglish
Pages (from-to)64-75
Number of pages12
JournalJournal of Theoretical Biology
Volume243
Issue number1
DOIs
Publication statusPublished - 2006 Nov 7
Externally publishedYes

Keywords

  • Complex networks
  • Contact process
  • Epidemic threshold
  • Epidemiology
  • Scale-free networks

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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