Evaluating Targeted Mobility Restrictions on COVID-19 Transmission in Seoul: A Metapopulation Modeling Study Using Mobile Phone Data

Yuna Lim; Jonggul Lee; Eunok Jung

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arXiv · 2603.16064

Evaluating Targeted Mobility Restrictions on COVID-19 Transmission in Seoul: A Metapopulation Modeling Study Using Mobile Phone Data

Yuna Lim·Jonggul Lee·Eunok Jung

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Abstract

Broad mobility restrictions can help control infectious disease spread, but their socioeconomic costs and the variation in transmission risks by mobility purpose, age group, and spatial connectivity highlight the need for targeted approaches. In this study, we developed an age-structured SEIR metapopulation model for COVID-19 across Seoul's 25 districts, integrating mobile phone-derived origin-destination data. We stratified mobility by age (0-19, 20-59, 60+) and purpose: residential (H), school/work (W), and other non-routine (O). Using 2024 mobility data as a baseline and incorporating pandemic-period (2020-2021) mobility deviations, we investigated counterfactual strategies under various targeting scenarios. Our results showed that W restrictions among adults aged 20-59 produced the highest per-capita reductions in infection. Spatial clustering based on population-adjusted W inflows showed that high-inflow central business districts corresponded to the fast-spreading districts identified in the simulations. Targeting W flows into and within this cluster consistently reduced epidemic size across uncertain seeding locations. Furthermore, weekday-inclusive schedules outperformed weekend-only restrictions. Overall, our findings suggest that although citywide restrictions achieve larger reductions, strategically targeting routine school/work mobility among adults aged 20-59 within fast-spreading clusters can provide substantial epidemiological benefits while reducing broader socioeconomic disruption.

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Yuna Lim, Jonggul Lee, Eunok Jung. 2026-03-17. Evaluating Targeted Mobility Restrictions on COVID-19 Transmission in Seoul: A Metapopulation Modeling Study Using Mobile Phone Data. https://arxiv.org/abs/2603.16064

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