  The widely spread CoronaVirus Disease (COVID)-19 is one of the worst
infectious disease outbreaks in history and has become an emergency of primary
international concern. As the pandemic evolves, academic communities have been
actively involved in various capacities, including accurate epidemic
estimation, fast clinical diagnosis, policy effectiveness evaluation and
development of contract tracing technologies. There are more than 23,000
academic papers on the COVID-19 outbreak, and this number is doubling every 20
days while the pandemic is still on-going [1]. The literature, however, at its
early stage, lacks a comprehensive survey from a data analytics perspective. In
this paper, we review the latest models for analyzing COVID19 related data,
conduct post-publication model evaluations and cross-model comparisons, and
collect data sources from different projects.
