Fu, HanHanFuChan, Ta-ChienTa-ChienChanWu, Chieh-YinChieh-YinWuTZAI-HUNG WENLan, Chih-ChanChih-ChanLanChou, Mei-FangMei-FangChouLim, Chen-FattChen-FattLimLee, Wei-SenWei-SenLeeChen, Wei-JenWei-JenChenLiu, Yu-LunYu-LunLiuCheng, Hao-YuanHao-YuanChengLin, Bo-ChengBo-ChengLinKuo, Fei-YingFei-YingKuoLin, Yu-XuanYu-XuanLinTsai, Yi-ChenYi-ChenTsaiChang, NingNingChangLin, Hsien-HoHsien-HoLinChang, Hsiao-HanHsiao-HanChang2025-12-122025-12-122023-04-0110232141https://www.scopus.com/pages/publications/85218832014https://scholars.lib.ntu.edu.tw/handle/123456789/734564Human mobility data are used to evaluate the effectiveness of crowd control measures and delineate geographical zones and other epidemic prevention decisions for the COVID-19 epidemic. This application experience also revealed the challenges faced by the application of human mobility data in infectious disease research and practice. For example, there is no available public data and its usage regulations and empirical data to support the research results of the relationship between human movement and epidemic. Moreover, the three parties - data provider, researcher, and practical worker - have no approach to communicating with each other. It is suggested that in the future, a tripartite collaboration mode can be developed in which major domestic telecommunications companies provide, the National Disaster Prevention Center handles and maintains, and the government and research institutions apply to the National Disaster Prevention Center for use; establish a complete data platform and strengthen instant communication and cooperation channels.falseBig dataCOVID-19Human mobility dataInfectious disease control[SDGs]SDG3[SDGs]SDG11journal article10.6288/TJPH.202304_42(2).1120102-s2.0-85218832014