@article{1447009,
      recid = {1447009},
      author = {Marques, Joao Alexandre Lobo, and Fong, Simon,},
      title = {Epidemic analytics for decision supports in COVID19 crisis  /},
      pages = {1 online resource (vi, 158 pages) :},
      abstract = {Covid-19 has hit the world unprepared, as the deadliest  pandemic of the century. Governments and authorities, as  leaders and decision makers fighting against the virus,  enormously tap on the power of AI and its data analytics  models for urgent decision supports at the greatest  efforts, ever seen from human history. This book showcases  a collection of important data analytics models that were  used during the epidemic, and discusses and compares their  efficacy and limitations. Readers who from both healthcare  industries and academia can gain unique insights on how  data analytics models were designed and applied on epidemic  data. Taking Covid-19 as a case study, readers especially  those who are working in similar fields, would be better  prepared in case a new wave of virus epidemic may arise  again in the near future.},
      url = {http://library.usi.edu/record/1447009},
      doi = {https://doi.org/10.1007/978-3-030-95281-5},
}