Authors
Rowan Wang, Baofeng Shi, Shenghan Li, Cong Xie, Qingpeng Zhang
Published in
JMIR mHealth and uHealth. Volume 14. Pages e64941. Nov 09, 2026. Epub Nov 09, 2026.
Abstract
Smartphone-delivered travel-record and health-code systems were deployed across China during the COVID-19 pandemic to classify risk based on travel history and regulate access to venues and travel. The geographic classification rule is a key design choice for these systems. A city-boundary rule (city-cut) is administratively convenient but may group areas with different mobility patterns under one risk label, whereas a fixed-radius rule (radius-cut) offers greater spatial precision but may exclude more distant areas connected by routine travel. Whether a classification rule aligns with observed mobility is a design question distinct from evaluating a deployed system's epidemiological effects.
This study examined whether observed mobility in Shanghai supported a city-cut or a radius-cut geographic design for a mobile health travel-record system and whether mobility-derived communities corresponded to administrative districts.
We conducted a retrospective observational analysis of 1.44 billion deidentified China Unicom mobility records collected for two 46-day periods in 2019 and 2020. The analytical unit was the daily origin-area movement proportion. We calculated within-Shanghai, confirmed external-city, and nonadjacent-district movement proportions; for each origin area, we paired them by calendar day. We constructed grid-level transition matrices, used spectral clustering followed by k-means++, and checked the groupings across repeated initializations and selected dates.
Across 46 daily city-average observations in 2019, the intracity proportion was 97.2% (95% t CI 97.1%-97.3%). Nonadjacent-district movement was 13.7% (95% t CI 13.5%-13.9%), compared with 2.8% (95% t CI 2.7%-2.9%) for confirmed external-city movement. A paired, 2-tailed Newey-West heteroskedasticity- and autocorrelation-consistent (HAC) t test (α=.05) gave a mean difference of 10.9 (95% HAC CI 10.7-11.1; P<.001) percentage points. At the Chongming border, the corresponding difference was -3.1 (95% HAC CI -4.1 to -2.1; P<.001) percentage points.
This study moves the question from postdeployment evaluation to predeployment design, treating geographic calibration as an upstream mobile health decision. It differs from prior work that assessed uptake, privacy, or epidemiological outcomes after fielding, and from mobility research that treated movement data as a predictor, an outcome, or a model input rather than as a criterion for choosing a boundary rule. The contribution is threefold: it formulates a boundary-fit question that can be answered before a system is built; it provides a reproducible, auditable workflow (daily origin-area proportions, a paired HAC-based comparison, and a mobility-clustering check) that agencies and developers can rerun on their own data; and it demonstrates the workflow from end to end in a large municipality. Practically, a city-level unit is a defensible starting reference where movement is predominantly internal, whereas border areas warrant separate local assessment. Agencies should document where a boundary fits local movement and where exceptions may be needed. Decisions about restrictions also require epidemiological evidence and assessment of proportionality, public acceptability, and social costs.
PMID:
43002743
Bibliographic data and abstract were imported from PubMed on 10 Nov 2026.
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