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.
Computational and Applied Mathematics, Institute for Computational Engineering and Sciences, University of Texas at Austin, 1 University Station, C0200, Austin, TX 78712, USA.
%0 Journal Article
%1 bansal2007wib
%A Bansal, Shweta
%A Grenfell, Bryan T
%A Meyers, Lauren Ancel
%D 2007
%J Journal of the Royal Society Interface
%K corepape
%N 16
%P 879--891
%R 10.1098/rsif.2007.1100
%T When individual behaviour matters: homogeneous and network models in epidemiology.
%U http://dx.doi.org/10.1098/rsif.2007.1100
%V 4
%X 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.
@article{bansal2007wib,
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.},
added-at = {2009-05-15T07:47:45.000+0200},
author = {Bansal, Shweta and Grenfell, Bryan T and Meyers, Lauren Ancel},
biburl = {https://www.bibsonomy.org/bibtex/27edafd99d56474a9e06ae9e2b0a976ae/ebo},
doi = {10.1098/rsif.2007.1100},
institution = {Computational and Applied Mathematics, Institute for Computational Engineering and Sciences, University of Texas at Austin, 1 University Station, C0200, Austin, TX 78712, USA.},
interhash = {efad5c60995b4cf8889995a6e77b9450},
intrahash = {7edafd99d56474a9e06ae9e2b0a976ae},
journal = {Journal of the Royal Society Interface},
journalkeywords = {Disease Transmission, Infectious; Epidemiologic Methods; Humans; Models, Biological; Models, Statistical},
keywords = {corepape},
month = Oct,
number = 16,
pages = {879--891},
pii = {080KX13732480384},
pmid = {17640863},
timestamp = {2009-05-15T07:47:45.000+0200},
title = {When individual behaviour matters: homogeneous and network models in epidemiology.},
url = {http://dx.doi.org/10.1098/rsif.2007.1100},
volume = 4,
year = 2007
}