Selected article for: "data analysis and Wuhan city"

Author: Shuja, J.; Alanazi, E.; Alasmary, W.; Alashaikh, A.
Title: COVID-19 Datasets: A Survey and Future Challenges
  • Cord-id: f9eqbaj5
  • Document date: 2020_5_26
  • ID: f9eqbaj5
    Snippet: In December 2019, a novel virus named as 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 most consequential global crisis after the World Wars. The first line of defense against the COVID-19 spread are the non-pharmaceutical measures like social distancing
    Document: In December 2019, a novel virus named as 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 most consequential global crisis after the World Wars. The first line of defense against the COVID-19 spread are the non-pharmaceutical measures like social distancing and personal hygiene. On the other hand, the medical service providers are the first responders for infected persons with severe symptoms of COVID-19. 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 and X-ray images, (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 review and critically analyze works in these directions that are accompanied by open-source data and code. 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 COVID-19.

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