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