The increasing agglomeration of people in dense urban areas coupled with the existence of efficient modes of transportation connecting such centers, make cities particularly vulnerable to the spread of epidemics. Here we develop a data-driven approach combines with a meta-population modeling to capture the interplay between population density, mobility and epidemic spreading. We study 163 cities, chosen from four different continents, and report a global trend where the epidemic risk induced by human mobility increases consistently in those cities where mobility flows are predominantly between high population density centers. We apply our framework to the spread of SARS-CoV-2 in the United States, providing a plausible explanation for the observed heterogeneity in the spreading process across cities. Based on this insight, we propose realistic mitigation strategies (less severe than lockdowns), based on modifying the mobility in cities. Our results suggest that an optimal control strategy involves an asymmetric policy that restricts flows entering the most vulnerable areas but allowing residents to continue their usual mobility patterns.The evolution of epidemic outbreaks in urban settings is known to stem from the interplay between demographic, structural, and economical characteristics. Here, the authors combine a data driven approach with meta-population modelling to show that the epidemic vulnerability of cities hinges on the morphology of human flows, and propose how a city’s mobility backbone could be modified to minimize the epidemic risk.
Interplay between population density and mobility in determining the spread of epidemics in cities