Modeling the Impact of Human Mobility on Influenza Transmission Dynamics in Urban Settings: A Data-Driven SEIR and Time-Varying transmission Analysis
Abstract
Human mobility is a key driver of infectious disease transmission, yet its role in shaping seasonal influenza dynamics remains insufficiently characterized, particularly in comparison to COVID-19. This study aims to quantify the association between mobility patterns and influenza transmissibility and to evaluate the potential impact of targeted mobility interventions. We conducted a state-level analysis in the United States by integrating mobility data from Google and Apple reports (February 2020 to January 2022) with weekly influenza-like illness data. The time-varying effective reproduction number (Rt) was estimated using a standard epidemiological framework. Cross-correlation analysis was applied to identify lag structures between mobility indicators and RtR_tRt, followed by linear mixed-effects modeling to quantify associations while accounting for spatial heterogeneity. Model performance was evaluated using the Akaike Information Criterion. Regression-derived coefficients were subsequently incorporated into a Susceptible–Exposed–Infectious–Recovered (SEIR) model to simulate intervention scenarios. Influenza transmissibility exhibited clear seasonal variation, with Rt exceeding 1.0 during early-season growth and declining below this threshold in later months. Mobility changes were found to precede fluctuations in Rt by 3 to 6 weeks, with lag patterns varying by activity type. Residential activity and walking were associated with reduced transmission, whereas park visits and grocery-related mobility were identified as significant drivers of increased Rt. The best-performing model achieved the lowest information criteria values and explained a substantial proportion of variability in transmission dynamics. Simulation results indicated that targeted reductions in high-impact mobility activities, particularly park-related mobility, could substantially suppress epidemic growth. Combined moderate interventions across multiple mobility types also demonstrated meaningful effects. Human mobility is a significant and temporally structured determinant of seasonal influenza transmission. Activity-specific mobility patterns provide a predictive window for intervention and offer actionable insights for public health strategies. Targeted, mobility-informed interventions may represent an effective and less disruptive approach to mitigating seasonal influenza outbreaks.