Human mobility data in the COVID-19 pandemic:characteristics,applications,and challenges

The COVID-19 pandemic poses unprecedented challenges around the world.Many studies have applied mobility data to explore spatiotemporal trends over time,investigate associations with other variables,and predict or simulate the spread of COVID-19.Our objective was to provide a comprehensive overview of human mobility open data to guide researchers and policymakers in conducting data-driven evaluations and decision-making for the COVID-19 pandemic and other infectious disease outbreaks.We summarized the mobility data usage in COVID-19 studies by reviewing recent publications on COVID-19 and human mobility from a data-oriented perspective.We identified three major sources of mobility data:public transit systems,mobile operators,and mobile phone applications.Four approaches have been commonly used to estimate human mobility:public transit-based flow,social activity patterns,index-based mobility data,and social media-derived mobility data.We compared mobility datasets’characteristics by assessing data privacy,quality,space–time coverage,high-performance data storage and processing,and accessibility.We also present challenges and future directions of using mobility data.This review makes a pivotal contribution to understanding the use of and access to human mobility data in the COVID-19 pandemic and future disease outbreaks.