Cities are complex systems whose characteristics impact the health of people who live in them. Nonetheless, urban determinants of health often vary within spatial scales smaller than the resolution of epidemiological datasets. Thus, as cities expand and their inequalities grow, the development of theoretical frameworks that explain health at the neighbourhood level is becoming increasingly critical. To this end, we developed a methodology that uses census data to introduce urban geography as a leading-order predictor in the spread of influenza-like pathogens. Here, we demonstrate our framework using neighbourhood-level census data for Guadalajara (GDL, Western Mexico). Our simulations show that daily mobility patterns can drive neighbourhood-level variations in the basic reproduction number R₀, which in turn give rise to robust spatiotemporal patterns in the spread of disease. To generalize our results, we ran simulations in hypothetical cities with the same population, area, schools and businesses as GDL but different land use zoning. Experiments in these synthetic cities demonstrate that the agglomeration of daily activities can influence the growth rate, size and timing of urban epidemics. Overall, these findings support the view that cities can be redesigned to limit the geographical scope of influenza-like outbreaks.

城市是复杂的系统，其特征会影响城市居民的健康。尽管如此，城市健康的决定因素通常在小于流行病学数据集分辨率的空间尺度内变化。因此，随着城市的扩张和不平等的加剧，解释邻里健康状况的理论框架的发展变得越来越重要。为此，我们开发了一种方法，该方法使用人口普查数据将城市地理学作为流感样病原体传播的先导预测因素引入。在这里，我们使用瓜达拉哈拉（GDL，西墨西哥）的社区级人口普查数据演示了我们的框架。我们的模拟表明，日常流动模式可以驱动基本繁殖数R _0的邻里水平变化。，这反过来在疾病传播中引起了强有力的时空格局。为了概括我们的结果，我们在假设的城市中进行了模拟，这些城市的人口，面积，学校和企业与GDL相同，但土地使用分区不同。在这些综合城市中进行的实验表明，日常活动的集聚会影响城市流行的增长率，规模和时间。总体而言，这些发现支持以下观点：可以重新设计城市，以限制类似流感的暴发的地理范围。