Currently submitted to: JMIR Public Health and Surveillance
Date Submitted: Apr 6, 2026
Open Peer Review Period: Apr 7, 2026 - Jun 2, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Community risk, mobility patterns, and COVID-19 hospitalization risk: A ZIP code–level analysis using mobile-device data
ABSTRACT
Background:
The COVID-19 pandemic revealed substantial disparities in health outcomes across U.S. communities. High-resolution mobility data offer new opportunities for real-time surveillance of behavioral responses, yet less is known about how precautionary behavior varied within cities and how these differences were associated with subsequent COVID-19 hospitalization risk.
Objective:
To assess how community-level risk shapes heterogeneity in precautionary mobility behavior within cities and whether these differences are associated with subsequent COVID-19 hospitalization risk.
Methods:
We analyzed anonymized mobile-device data from 824 ZIP codes in the 20 most populous counties in Texas and COVID-19 hospitalization records from 620 ZIP codes (February–July 2020). We constructed a ZIP code–level measure of precautionary mobility reduction (PMR) and linked it to a composite community risk score derived from CDC social vulnerability indicators. Generalized additive mixed models estimated associations among community risk, mobility patterns, and weekly hospitalization rates, adjusting for epidemic timing, population size, and spatial clustering.
Results:
Mobility declined following statewide stay-at-home orders but varied substantially across ZIP codes. On April 2, 2020, PMR was negatively associated with community risk (slope −0.16; 95% CI −0.19 to −0.13; p<.001). At peak divergence, high-risk ZIP codes reduced mobility by 12–13 percentage points less than low-risk ZIP codes (95% CI 11–15). Greater PMR was associated with lower subsequent hospitalization risk (IRR=0.41; 95% CI=0.31–0.53), whereas higher community risk was associated with increased hospitalization risk (IRR=3.03; 95% CI=2.57–3.58). The protective association of mobility reduction was attenuated in higher-risk communities (interaction IRR=2.79; 95% CI=2.08–3.73).
Conclusions:
Mobility data can support real-time surveillance of behavioral responses during epidemics. Within-city differences in mobility reduction were associated with subsequent hospitalization risk, but their protective effect was weaker in higher-risk communities. Integrating digital mobility data with community risk metrics may improve targeted and equitable public health responses.
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