When individual behaviour matters: homogeneous and network models in epidemiology

By Shweta Bansal, Bryan T. Grenfell, Lauren Ancel Meyers. Journal of the Royal Society Interface, 2007

766 citations recorded by the source corpus.

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.

Subfields: Experimental and Cognitive Psychology, Modeling and Simulation, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/ca434e04e77de93f6a1fdde7325fbe209aefc5fb

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Citations in the corpus

Statistical physics of vaccination (2016, methodology): “on networks representing social contacts, and the methods of statistical physics concerned with the properties of networks could therefore be very useful [397].”
Mathematical models to characterize early epidemic growth: A Review (2016, background): “This approach and others have been used to demonstrate the dramatic effects that the degree distribution can have on disease spread in a population: homogeneous populations (with low variance degree distributions) are characterized by a high epidemic threshold and experience a lag before reaching th”
Constructing, conducting and interpreting animal social network analysis (2015, background): “If not, dendrograms may be highly misleading (Whitehead 2008).”
Networks and the Epidemiology of Infectious Disease (2010, methodology): “this and S∞are known, the expected attack rate is R∞= 1 −S∞. 4.2.3 Approximate Final Size The main way to calculate approximate ﬁnal sizes is given by p ercolation-based methods. These were reviewedbyBansal et al. (2007) and also in Newman(2010). Suppose we remove a fraction ϕ of links from the netw”
Modeling infectious disease dynamics in the complex landscape of global health (2015, neutral): “Increasing sophistication of contact network models (42), together with data on epidemiological contacts, creates opportunities for understanding and controlling transmission at a fundamental level (43) and opens up the possibility of independent study of relevant social factors (10).”