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 R 0 , 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.

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 neighborhood 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 neighborhood-level census data for Guadalajara (GDL, Western Mexico). Our simulations were calibrated using weekly hospitalization data from the 2009 A/H1N1 influenza pandemic and show that daily mobility patterns drive neighborhood-level variations in the basic reproduction number R 0 , 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. Our results demonstrate that the agglomeration of daily activities can largely influence the growth rate, size and timing of urban epidemics. Overall, these findings support the view that cities can be redesigned to limit the geographic scope of influenza-like outbreaks and provide a general mathematical framework to study the mechanisms by which local and remote health consequences result from characteristics of the physical environment. Introduction 1 Empirical studies have identified inter-city variations in the timing, intensity and 2 severity of influenza-like outbreaks [1-4]. Aiming to understand the mechanisms 3 through which city characteristics yield such health consequences, epidemiologists have 4 resourced to a variety of methods. Epidemiological data reveal compelling statistical 5 correlations, but do not resolve intra-city variations in health that are driven by lifestyle 6 inequalities at the neighborhood level [2, 4-6]. In contrast, agent-based computational 7 models use massive mobility datasets to recreate the behavior of individuals as they 8 September 11, 2019 1/15interact and spread infections [7][8][9][10][11][12]. Even though this approach allows for 9 household-level analyses, its elevated complexity obscures relationships between model 10 parameters and their health impacts, making it inadequate for the development of 11 general strategies in public health [13]. Furthermore, agent-based simulations are costly 12 and the information necessary to calibrate them is not openly available for most of the 13 world's cities. Thus, despite the fact that epidemics have the potential to be seeded 14 anywhere, these intricate models have been overwhelmingly applied to populations in 15 the developed world. Meanwhile, the fundamental mechanisms that drive health 16 inequalities within metropolitan areas remain elusive. 17Urban design determines the densities and relative locations o...