When individual behaviour matters: Homogeneous and network models in epidemiology

Shweta Bansal, Bryan T. Grenfell, Lauren Ancel Meyers

Research output: Contribution to journalArticle

318 Scopus citations

Abstract

Heterogeneity in host contact patterns profoundly shapes population-level disease dynamics. Many epidemiological models make simplifying assumptions about the patterns of disease-causing interactions among hosts. In particular, homogeneous-mixing models assume that all hosts have identical rates of disease-causing contacts. In recent years, several network-based approaches have been developed to explicitly model heterogeneity in host contact patterns. Here, we use a network perspective to quantify the extent to which real populations depart from the homogeneous-mixing assumption, in terms of both the underlying network structure and the resulting epidemiological dynamics. We find that human contact patterns are indeed more heterogeneous than assumed by homogeneous-mixing models, but are not as variable as some have speculated. We then evaluate a variety of methodologies for incorporating contact heterogeneity, including network-based models and several modifications to the simple SIR compartmental model. We conclude that the homogeneous-mixing compartmental model is appropriate when host populations are nearly homogeneous, and can be modified effectively for a few classes of non-homogeneous networks. In general, however, network models are more intuitive and accurate for predicting disease spread through heterogeneous host populations.

Original languageEnglish (US)
Pages (from-to)879-891
Number of pages13
JournalJournal of the Royal Society Interface
Volume4
Issue number16
DOIs
StatePublished - Oct 22 2007
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Biophysics
  • Bioengineering
  • Biomaterials
  • Biochemistry
  • Biomedical Engineering

Keywords

  • Compartmental model
  • Contact network
  • Epidemic model
  • Homogeneous-mixing

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