COVID-19 open source data sets: a comprehensive survey.

Content Provider	Europe PMC
Author	Shuja, Junaid Alanazi, Eisa Alasmary, Waleed Alashaikh, Abdulaziz
Abstract	In December 2019, a novel virus named COVID-19 emerged in the city of Wuhan, China. In early 2020, the COVID-19 virus spread in all continents of the world except Antarctica, causing widespread infections and deaths due to its contagious characteristics and no medically proven treatment. The COVID-19 pandemic has been termed as the most consequential global crisis since the World Wars. The first line of defense against the COVID-19 spread are the non-pharmaceutical measures like social distancing and personal hygiene. The great pandemic affecting billions of lives economically and socially has motivated the scientific community to come up with solutions based on computer-aided digital technologies for diagnosis, prevention, and estimation of COVID-19. Some of these efforts focus on statistical and Artificial Intelligence-based analysis of the available data concerning COVID-19. All of these scientific efforts necessitate that the data brought to service for the analysis should be open source to promote the extension, validation, and collaboration of the work in the fight against the global pandemic. Our survey is motivated by the open source efforts that can be mainly categorized as (a) COVID-19 diagnosis from CT scans, X-ray images, and cough sounds, (b) COVID-19 case reporting, transmission estimation, and prognosis from epidemiological, demographic, and mobility data, (c) COVID-19 emotional and sentiment analysis from social media, and (d) knowledge-based discovery and semantic analysis from the collection of scholarly articles covering COVID-19. We survey and compare research works in these directions that are accompanied by open source data and code. Future research directions for data-driven COVID-19 research are also debated. We hope that the article will provide the scientific community with an initiative to start open source extensible and transparent research in the collective fight against the COVID-19 pandemic.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC7503433&blobtype=pdf
ISSN	0924669X
Journal	Applied Intelligence (Dordrecht, Netherlands) [Appl Intell (Dordr)]
Volume Number	51
DOI	10.1007/s10489-020-01862-6
PubMed Central reference number	PMC7503433
Issue Number	3
PubMed reference number	34764552
e-ISSN	15737497
Language	English
Publisher	Springer US
Publisher Date	2020-09-21
Publisher Place	New York
Access Restriction	Open
Rights License	This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. © Springer Science+Business Media, LLC, part of Springer Nature 2020
Subject Keyword	COVID-19 Coronavirus Pandemic Machine learning Artificial intelligence Open source Data sets
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence