When individual behaviour matters: homogeneous and network models in epidemiology

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.

The effect of individual variation on the structure and function of interaction networks in harvester ants

“…Networks exhibiting heterogeneity in connectivity have a right-skewed degree distribution: a few individuals have many connections, and most individuals have few connections. Such networks with highly connected nodes, or hubs, facilitate rapid disease transfer [31,32], efficient air-traffic flow [33], problemsolving in human groups [1] and the rate of neuron activation, synchronization and response times in neural networks [34]. Other attributes of network structure, beyond the scope of the present work, may also expedite or slow down the rate of information transfer [1,34].…”

Individual differences in boldness influence patterns of social interactions and the transmission of cuticular bacteria among group-mates

“…disease outbreaks) are influenced by the connectivity of individuals [15][16][17]. The incorporation of social network theory into models of disease dynamics has shown that the structure of the interaction network and the processes that underlies its formation can affect transmission rate [18][19][20]. Therefore, empirical studies of the processes that drive social network formation are crucial for our understanding of disease dynamics.…”

Personality composition alters the transmission of cuticular bacteria in social groups

“…Likewise, populations harbouring a greater diversity of genotypes [8], behavioural phenotypes [9] and patterns of social interactions [10,11] can reduce the spread of infectious agents. However, few studies have compared the relative influence of the individual traits of the index case versus the collective traits of the susceptible population (i.e.…”