Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 6 July 2020 / Approved: 7 July 2020 / Online: 7 July 2020 (10:06:05 CEST)

How to cite: Mao, Y.; Jiang, S.; Nametz, D. Data-driven Analytical Models of COVID-2019 for Epidemic Prediction, Clinical Diagnosis, Policy Effectiveness and Contact Tracing: A Survey. Preprints 2020, 2020070124 (doi: 10.20944/preprints202007.0124.v1). Mao, Y.; Jiang, S.; Nametz, D. Data-driven Analytical Models of COVID-2019 for Epidemic Prediction, Clinical Diagnosis, Policy Effectiveness and Contact Tracing: A Survey. Preprints 2020, 2020070124 (doi: 10.20944/preprints202007.0124.v1).

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.

Subject Areas

COVID-19; Epidemic Prediction; Clinical Diagnosis; Policy Effectiveness; Contact Tracing

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