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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 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. 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.Author summaryEnvironmental, social and economic factors give rise to health inequalities among the inhabitants of a city, prompting researchers to propose ’smart’ urban planning as a tool for public health. Here, we present a mathematical framework that relates the spatial distributions of schools and economic activities to the spatiotemporal spread of influenza-like outbreaks. First, we calibrated our model using city-wide data for Guadalajara (GDL, Western Mexico) and found that a person’s place of residence can largely influence their role and vulnerability during an epidemic. In particular, the higher contact rates of people living near major activity hubs can give rise to predictable patterns in the spread of disease. To test the universality of our findings, we ’redesigned’ GDL by redistributing houses, schools and businesses across the city and ran simulations in the resulting geographies. Our results suggest that, through its impact on the agglomeration of economic activities, urban planning may be optimized to inhibit epidemic growth. By predicting health inequalities at the neighborhood-level, our methodology may help design public health strategies that optimize resources and target those who are most vulnerable. Moreover, it provides a mathematical framework for the design and analysis of experiments in urban health research. |
