Mobile Phone-Based Population Flow Data for the COVID-19 Outbreak in Mainland China

• Main Authors: Xin Lu, Jing Tan, Ziqiang Cao, Yiquan Xiong, Shuo Qin, Tong Wang, Chunrong Liu, Shiyao Huang, Wei Zhang, Laurie B. Marczak, Simon I. Hay, Lehana Thabane, Gordon H. Guyatt, Xin Sun
• Format: Article
• Language: English
• Published: American Association for the Advancement of Science (AAAS) 2021-01-01
• Series: Health Data Science
• Online Access: http://dx.doi.org/10.34133/2021/9796431

Background. Human migration is one of the driving forces for amplifying localized infectious disease outbreaks into widespread epidemics. During the outbreak of COVID-19 in China, the travels of the population from Wuhan have furthered the spread of the virus as the period coincided with the world’s largest population movement to celebrate the Chinese New Year. Methods. We have collected and made public an anonymous and aggregated mobility dataset extracted from mobile phones at the national level, describing the outflows of population travel from Wuhan. We evaluated the correlation between population movements and the virus spread by the dates when the number of diagnosed cases was documented. Results. From Jan 1 to Jan 22 of 2020, a total of 20.2 million movements of at-risk population occurred from Wuhan to other regions in China. A large proportion of these movements occurred within Hubei province (84.5%), and a substantial increase of travels was observed even before the beginning of the official Chinese Spring Festival Travel. The outbound flows from Wuhan before the lockdown were found strongly correlated with the number of diagnosed cases in the destination cities (log-transformed). Conclusions. The regions with the highest volume of receiving at-risk populations were identified. The movements of the at-risk population were strongly associated with the virus spread. These results together with province-by-province reports have been provided to governmental authorities to aid policy decisions at both the state and provincial levels. We believe that the effort in making this data available is extremely important for COVID-19 modelling and prediction.