Volz Erik, Meyers Lauren Ancel
Department of Integrative Biology, University of Texas at Austin, 1 University Station, C0930, Austin, TX 78712, USA.
Proc Biol Sci. 2007 Dec 7;274(1628):2925-33. doi: 10.1098/rspb.2007.1159.
Contact patterns in populations fundamentally influence the spread of infectious diseases. Current mathematical methods for epidemiological forecasting on networks largely assume that contacts between individuals are fixed, at least for the duration of an outbreak. In reality, contact patterns may be quite fluid, with individuals frequently making and breaking social or sexual relationships. Here, we develop a mathematical approach to predicting disease transmission on dynamic networks in which each individual has a characteristic behaviour (typical contact number), but the identities of their contacts change in time. We show that dynamic contact patterns shape epidemiological dynamics in ways that cannot be adequately captured in static network models or mass-action models. Our new model interpolates smoothly between static network models and mass-action models using a mixing parameter, thereby providing a bridge between disparate classes of epidemiological models. Using epidemiological and sexual contact data from an Atlanta high school, we demonstrate the application of this method for forecasting and controlling sexually transmitted disease outbreaks.
人群中的接触模式从根本上影响传染病的传播。当前用于网络流行病学预测的数学方法大多假定个体之间的接触是固定的，至少在疫情爆发期间如此。实际上，接触模式可能相当多变，个体经常建立和断绝社会关系或性关系。在此，我们开发了一种数学方法来预测动态网络中的疾病传播，其中每个个体都有一个特征行为（典型接触数），但其接触对象的身份会随时间变化。我们表明，动态接触模式塑造流行病学动态的方式无法在静态网络模型或质量作用模型中得到充分体现。我们的新模型使用一个混合参数在静态网络模型和质量作用模型之间进行平滑插值，从而在不同类别的流行病学模型之间架起一座桥梁。利用来自亚特兰大一所高中的流行病学和性接触数据，我们展示了该方法在预测和控制性传播疾病爆发方面的应用。
