Understanding Human Mobility Patterns under a Public Health Emergency

Background: Understanding human mobility changes during epidemics is critical for predicting disease spread and planning interventions. However, capturing fine-scale dynamics is challenging. Methods: This study analyzed high-resolution human mobility patterns in Shanghai, China, during the 2022 SARS-CoV-2 Omicron BA.2 outbreak using large-scale anonymized cellular signaling data. We investigated mobility shifts across five distinct epidemic phases (pre-outbreak, targeted interventions, citywide lockdown, targeted lifting, and reopening) stratified by age, sex, and travel purpose. A comprehensive evaluation of four gravity and four radiation spatial interaction models was conducted to assess their ability to explain the observed mobility patterns under varying demographic and behavioral conditions. Results: Population size and distance were found to be primary drivers of mobility, with notable variations across demographic groups and travel purposes. During the lockdown, mobility significantly decreased, particularly for social-related trips and the working-age population, while the effect of distance was substantially higher. Although mobility volumes recovered post-lockdown, a larger effect of distance persisted, implying long-lasting behavioral changes. Our comparative analysis showed that while several variants of gravity and radiation models captured overall patterns effectively, their performance was context-dependent, varying significantly across epidemic phases, population subgroups, and travel purposes. Conclusion: These findings highlight the importance of integrating different mobility models to capture the complex human mobility picture by different population groups during an epidemic outbreak. Overall, this study advances our understanding of behavioral adaptations during crises, enhancing preparedness and response planning.

Infectious Disease Modelling [Vol 11, (Issue 1)]