Impact of Human Mobility on COVID-19 Transmission According to Mobility Distance, Location, and Demographic Factors in the Greater Bay Area of China: Population-Based Study

BACKGROUND: Mobility restriction was one of the primary measures used to restrain the spread of COVID-19 globally. Governments implemented and relaxed various mobility restriction measures in the absence of evidence for almost 3 years, which caused severe adverse outcomes in terms of health, society, and economy. OBJECTIVE: This study aimed to quantify the impact of mobility reduction on COVID-19 transmission according to mobility distance, location, and demographic factors in order to identify hotspots of transmission and guide public health policies. METHODS: Large volumes of anonymized aggregated mobile phone position data between January 1 and February 24, 2020, were collected for 9 megacities in the Greater Bay Area, China. A generalized linear model (GLM) was established to test the association between mobility volume (number of trips) and COVID-19 transmission. Subgroup analysis was also performed for sex, age, travel location, and travel distance. Statistical interaction terms were included in a variety of models that express different relations between involved variables. RESULTS: The GLM analysis demonstrated a significant association between the COVID-19 growth rate ratio (GR) and mobility volume. A stratification analysis revealed a higher effect of mobility volume on the COVID-19 GR among people aged 50-59 years (GR decrease of 13.17% per 10% reduction in mobility volume; P<.001) than among other age groups (GR decreases of 7.80%, 10.43%, 7.48%, 8.01%, and 10.43% for those aged ≤18, 19-29, 30-39, 40-49, and ≥60 years, respectively; P=.02 for the interaction). The impact of mobility reduction on COVID-19 transmission was higher for transit stations and shopping areas (instantaneous reproduction number [R(t)] decreases of 0.67 and 0.53 per 10% reduction in mobility volume, respectively) than for workplaces, schools, recreation areas, and other locations (R(t) decreases of 0.30, 0.37, 0.44, and 0.32, respectively; P=.02 for the interaction). The association between mobility volume reduction and COVID-19 transmission was lower with decreasing mobility distance as there was a significant interaction between mobility volume and mobility distance with regard to R(t) (P<.001 for the interaction). Specifically, the percentage decreases in R(t) per 10% reduction in mobility volume were 11.97% when mobility distance increased by 10% (Spring Festival), 6.74% when mobility distance remained unchanged, and 1.52% when mobility distance declined by 10%. CONCLUSIONS: The association between mobility reduction and COVID-19 transmission significantly varied according to mobility distance, location, and age. The substantially higher impact of mobility volume on COVID-19 transmission for longer travel distance, certain age groups, and specific travel locations highlights the potential to optimize the effectiveness of mobility restriction strategies. The results from our study demonstrate the power of having a mobility network using mobile phone data for surveillance that can monitor movement at a detailed level to measure the potential impacts of future pandemics.