Network--based epidemic models that account for heterogeneous contact patterns are extensively used to predict and control the diffusion of infectious diseases. We use census and survey data to reconstruct a geo--referenced and age--stratified synthetic urban population connected by stable social relations. We consider two kinds of interactions, distinguishing daily (household) contacts from other frequent contacts. Moreover, we allow any couple of individuals to have rare fortuitous interactions. We simulate the epidemic diffusion on a synthetic urban network for a typical medium-size Italian city and characterize the outbreak speed, pervasiveness, and predictability in terms of the socio--demographic and geographic features of the host population. Introducing age--structured contact patterns results in faster and more pervasive outbreaks, while assuming that the interaction frequency decays with distance has only negligible effects. Preliminary evidence shows the existence of patterns of hierarchical spatial diffusion in urban areas, with two regimes for epidemic spread in low- and high-density regions.

翻译：基于网络的流行病模型通过考虑异质性接触模式，被广泛用于预测和控制传染病的扩散。我们利用人口普查和调查数据，重建了一个由稳定社会关系连接、具有地理参照和年龄分层的合成城市人口。我们考虑两种类型的互动：区分日常（家庭）接触与其他频繁接触。此外，我们允许任意两个个体之间发生罕见的偶然互动。我们在一个典型中型意大利城市的合成城市网络上模拟流行病扩散，并根据宿主人口的社会人口学与地理特征刻画疫情暴发的速度、渗透性及可预测性。引入年龄结构化的接触模式会导致疫情暴发更快、更广泛，而假设互动频率随距离衰减则仅产生可忽略的影响。初步证据表明，城市区域存在层级化空间扩散模式，并在低密度和高密度区域呈现两种不同的流行病传播动态。