Mobile phone data have played a key role in quantifying human mobility during the COVID-19 pandemic. Existing studies on mobility patterns have primarily focused on regional aggregates in high-income countries, obfuscating the accentuated impact of the pandemic on the most vulnerable populations. By combining geolocation data from mobile phones and population census for 6 middle-income countries across 3 continents between March and December 2020, we uncovered common disparities in the behavioral response to the pandemic across socioeconomic groups. When the pandemic hit, urban users living in low-wealth neighborhoods were less likely to respond by self-isolating at home, relocating to rural areas, or refraining from commuting to work. The gap in the behavioral responses between socioeconomic groups persisted during the entire observation period. Among low-wealth users, those who used to commute to work in high-wealth neighborhoods pre-pandemic were particularly at risk, facing both the reduction in activity in high-wealth neighborhood and being more likely to be affected by public transport closures due to their longer commute. While confinement policies were predominantly country-wide, these results suggest a role for place-based policies informed by mobility data to target aid to the most vulnerable.

翻译：手机数据在量化COVID-19疫情期间人类出行行为中发挥了关键作用。现有出行模式研究主要聚焦于高收入国家的区域集聚特征，模糊了疫情对最脆弱群体的加重影响。通过整合2020年3月至12月间三大洲六个中等收入国家的手机地理定位数据与人口普查数据，我们发现不同社会经济群体对疫情的行为响应存在共性差异。疫情暴发时，居住在低财富社区的城市用户更不可能通过居家自我隔离、迁往农村或减少通勤工作做出响应。社会经济群体间的行为响应差距在整个观测期内持续存在。在低财富用户中，疫情前曾通勤至高财富社区工作的群体面临双重风险：既因高财富社区经济活动减少而受损，又因通勤距离更长更易受到公共交通停运的影响。尽管封锁政策以全国性为主，但研究结果表明，基于出行数据的地理定向政策可更有效地向最脆弱群体提供精准帮扶。