Epidemics spread through the same networks that organize daily urban life. Mobility data makes it possible to reconstruct those networks and study how infections move through households, schools, workplaces, and community spaces [1, 2].
At SUNLab, we use large-scale mobility data to build detailed agent-based epidemic models of cities. These models estimate where transmission happens, how contact patterns change over time, and which interventions can reduce spread while minimizing social and economic disruption [1–3].
Our work showed that strong testing, contact tracing, and household quarantine can support reopening after lockdowns while keeping transmission and hospital demand at manageable levels [2]. We also found that transmission is highly uneven: a small fraction of individuals generate most infections, but the bulk of spread often occurs not in mass gatherings alone, but across smaller events in places such as workplaces, grocery stores, and food venues [1, 2]. In related work, we show that epidemic–economic trade-offs are unequally distributed across income groups and occupations, with low-income, in-person workers facing the strongest trade-offs during COVID-19 [3].
These results help move beyond blanket restrictions toward more targeted epidemic mitigation. By identifying when and where risk concentrates, mobility-based models provide tools to design interventions that are both more effective and less disruptive [1–3].
References
Aleta, A., Martín-Corral, D., Bakker, M. A., Pastore Y Piontti, A., Ajelli, M., Litvinova, M., Chinazzi, M., Dean, N. E., Halloran, M. E., Longini, I. M., Pentland, A., Vespignani, A., Moreno, Y., & Moro, E. (2022). Quantifying the importance and location of SARS-CoV-2 transmission events in large metropolitan areas. Proceedings of the National Academy of Sciences, 119, e2112182119.
Aleta, A., Martin-Corral, D., Pastore y Piontti, A., Ajelli, M., Litvinova, M., Chinazzi, M., Dean, N. E., Halloran, M. E., Longini, I. M., & Merler, S. (2020). Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19. Nature Human Behaviour, 4(9), 964–971. https://www.nature.com/articles/s41562-020-0931-9
Pangallo, M., Aleta, A., Del Rio-Chanona, R. M., Pichler, A., Martín-Corral, D., Chinazzi, M., Lafond, F., Ajelli, M., Moro, E., Moreno, Y., Vespignani, A., & Farmer, J. D. (2023). The unequal effects of the health–economy trade-off during the COVID-19 pandemic. Nature Human Behaviour.