Integrating Cellular Mobility With Geographic Topology for Real-Time Pandemic Surveillance

Abstract

While facilitating communication services, cellular mobile networks also provide real-time and large-scale observations of individual mobility, offering significant potential far beyond their traditional connectivity role. This paper proposes a mobility-aware epidemiological framework that integrates the Susceptible-Exposed-Infectious-Recovered (SEIR) model with mobility data derived from cellular mobile networks to improve predictive accuracy and applicability. By incorporating fine-grained, cell-level mobility data, our approach captures localized movement patterns and their impact on epidemic dynamics in urban environments. Simulation results show that areas with high mobility and density reach infection peaks 66–70% earlier and over 2.5 times higher than predictions obtained using a standard SEIR model, while low-mobility regions exhibit minimal deviation from baseline behavior. These findings illustrate that network-level mobility indicators are effective tools for identifying localized epidemic risks, supporting targeted and real-time public health interventions.