Human mobility fundamentally shapes the spatial spread of infectious diseases, yet the level of detail required from mobility data to accurately inform epidemic models remains unclear. Mobile phone records offer unprecedented resolution on population movements, but little attention has been devoted however to determining (i) which aspects of mobility are epidemiologically relevant and (ii) what level of data resolution is necessary to capture spatial invasion dynamics. Using mobile phone records from 9.5 million users in Senegal (approximately 80% of the population), we systematically compare three approaches to aggregating mobility data for epidemic modeling. These approaches span a range of resolutions: high-resolution tracking of all individual displacements between consecutive visited locations (HR), medium-resolution accounting for time spent in all visited locations (MR), and low-resolution identification of the most-visited location (LR). We incorporate these mobility representations into a metapopulation epidemic model that explicitly accounts for transmission from residents, visitors, and returning travelers, and simulate diseases with varying transmissibility corresponding to controlled epidemic conditions, seasonal influenza–like transmission, and highly transmissible pathogens. We find that preserving all observed displacements in individual trajectories does not necessarily improve the epidemiological relevance of mobility in pathogens spatial transmission. Instead, displacement-based networks fragment long-range trips and underestimate key spatial connections relevant for disease spread. In contrast, approaches that capture where individuals spend most of their time (such as home, work, or school) more accurately reproduce spatial invasion patterns. Accounting for additional daily activities beyond these primary locations provides little additional epidemiological information. Our results suggest that lower-resolution mobility indicators capturing time spent at key locations are sufficient to inform predictive epidemic models. These findings have important implications for both epidemic modeling and data governance, indicating that mobile phone data can be aggregated to reduce privacy issues while still providing the essential information needed to model spatial disease transmission.
Pullano et al. (Thu,) studied this question.