ArticleScientific reports2026
Complex network analysis of mobility dynamics in Seoul during the COVID-19 pandemic 2020-2022.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Recent advances in geolocated data enable quantitative analysis of human mobility, critical for urban planning and epidemic control. This study examines mobility dynamics in Seoul during the COVID-19 pandemic, where minimal lockdown measures allowed relatively natural movement patterns, offering insights for managing viral spread with reduced socioeconomic disruption. We analyzed the Seoul “Living Mobility” dataset (January 2020–December 2022), which contains monthly origin-to-destination flows among 424 sub-districts (“dong”) in 25 districts (“gu”). We constructed a directed and weighted mobility network based on monthly trips between every pair of dongs. We assessed (i) local node intensity via weighted density within and across districts, (ii) global connectivity through distribution of node strengths, (iii) node clusters via community detection with the Infomap community-detection algorithm, and (iv) the top and bottom 10 hub nodes using weighted PageRank centrality. From 2020 to 2022, within-gu weighted density increased by 21.7%, and across-gu density by 13.0%. Node strength distribution showed 138 high-mobility dongs (33%) driving 56% of total flows. Community detection identified 11 clusters transcending district boundaries, with within-community mobility 2–5 times higher than cross-community flows. High-mobility hubs, such as Yeoksam-dong (Gangnam-gu) and Yeoui-dong (Yeongdeungpo-gu), exhibited up to 70 times more movement than peripheral areas. Seoul’s mobility network remained fully connected yet highly heterogeneous during the COVID-19 pandemic years, with central hubs and clusters dominating movement. The rising saturation of within-dong flows reflects intensified local interactions, highlighting the growing importance of clustering and localized hotspots in driving future transmission risks. Targeting interventions at the 138 high-mobility dongs—and at bridges between their communities—can optimize disease control while minimizing disruption. Low-mobility dongs highlight potential service access disparities, guiding tailored interventions for equitable urban planning and public health strategies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.