Bansal Shweta, Grenfell Bryan T, Meyers Lauren Ancel
Computational and Applied Mathematics, Institute for Computational Engineering and Sciences, University of Texas at Austin, 1 University Station, C0200, Austin, TX 78712, USA.
J R Soc Interface. 2007 Oct 22;4(16):879-91. doi: 10.1098/rsif.2007.1100.
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.
宿主接触模式的异质性深刻地塑造了群体层面的疾病动态。许多流行病学模型对宿主间致病相互作用的模式做出了简化假设。特别是，均匀混合模型假定所有宿主具有相同的致病接触率。近年来，已开发出几种基于网络的方法来明确模拟宿主接触模式的异质性。在此，我们从网络角度，就基础网络结构和由此产生的流行病学动态，量化实际群体偏离均匀混合假设的程度。我们发现，人类接触模式确实比均匀混合模型所假定的更具异质性，但并不像一些人推测的那样多变。然后，我们评估了多种纳入接触异质性的方法，包括基于网络的模型以及对简单SIR compartmental模型的几种修改。我们得出结论，当宿主群体几乎均匀时，均匀混合compartmental模型是合适的，并且对于几类非均匀网络可以有效地进行修改。然而，总体而言，网络模型在预测疾病在异质宿主群体中的传播方面更直观且准确。
