@article{499,
  author = {Yudong Tao and Chuang Yang and Tianyi Wang and Erik Coltey and Yanxiu Jin and Yinghao Liu and Renhe Jiang and Zipei Fan and Xuan Song and Ryosuke Shibasaki and Shu-Ching Chen and Mei-Ling Shyu and Steven Luis},
  title = {A Survey on Data-driven COVID-19 and Future Pandemic Management},
  abstract = {The COVID-19 pandemic has resulted in more than 440 million confirmed cases globally and almost 6 million reported deaths as of March 2022. Consequently, the world experienced grave repercussions to citizens’ lives, health, wellness, and the economy. In responding to such a disastrous global event, countermeasures are often implemented to slow down and limit the virus’s rapid spread. Meanwhile, disaster recovery, mitigation, and preparation measures have been taken to manage the impacts and losses of the ongoing and future pandemics. Data-driven techniques have been successfully applied to many domains and critical applications in recent years. Due to the highly interdisciplinary nature of pandemic management, researchers have proposed and developed data-driven techniques across various domains. However, a systematic and comprehensive survey of data-driven techniques for pandemic management is still missing. In this article, we review existing data analysis and visualization techniques and their applications for COVID-19 and future pandemic management with respect to four phases (namely, Response, Recovery, Mitigation, and Preparation) in disaster management. Data sources utilized in these studies and specific data acquisition and integration techniques for COVID-19 are also summarized. Furthermore, open issues and future directions for data-driven pandemic management are discussed.},
  year = {2023},
  journal = {ACM Computing Surveys},
  volume = {55},
  chapter = {1},
  pages = {36},
  month = {07},
  issn = {0360-0300},
  url = {https://par.nsf.gov/biblio/10436850},
  doi = {10.1145/3542818},
}