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

Data-driven Analytical Models of COVID-2019 for Epidemic Prediction, Clinical Diagnosis, Policy Effectiveness and Contact Tracing: A Survey