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Related Experiment Video

Updated: Oct 13, 2025

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples
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COVID-19 open source data sets: a comprehensive survey.

Junaid Shuja1,2,3, Eisa Alanazi4,3, Waleed Alasmary2,3

  • 1Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Islamabad, Pakistan.

Applied Intelligence (Dordrecht, Netherlands)
|November 12, 2021
PubMed
Summary
This summary is machine-generated.

This survey reviews open-source digital technologies for COVID-19 diagnosis, transmission estimation, and sentiment analysis. It highlights the need for open data and code to advance research and collaboration against the pandemic.

Keywords:
Artificial intelligenceCOVID-19CoronavirusData setsMachine learningOpen sourcePandemic

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Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
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Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Public Health

Background:

  • The COVID-19 pandemic, caused by a novel virus, emerged in late 2019 and rapidly spread globally, presenting unprecedented health and socioeconomic challenges.
  • Non-pharmaceutical interventions like social distancing and hygiene remain the primary defense, but digital technologies are crucial for diagnosis, prevention, and management.
  • The scientific community's response includes AI and statistical analysis of COVID-19 data, emphasizing the critical need for open-source data and code.

Purpose of the Study:

  • To survey and compare research efforts utilizing open-source data and code for various COVID-19 related applications.
  • To categorize open-source initiatives into key areas: diagnosis, epidemiological analysis, sentiment analysis, and knowledge discovery.
  • To stimulate further open-source, transparent, and collaborative research in combating the COVID-19 pandemic.

Main Methods:

  • Systematic review and comparison of research works focused on open-source data and code for COVID-19.
  • Categorization of studies into four main themes: (a) diagnosis (CT, X-ray, cough sounds), (b) case reporting and transmission estimation, (c) social media sentiment analysis, and (d) knowledge discovery from scholarly articles.
  • Analysis of research accompanied by publicly available datasets and source code.

Main Results:

  • Identified significant open-source efforts in AI-driven COVID-19 diagnosis using medical imaging and audio data.
  • Reviewed studies employing epidemiological, demographic, and mobility data for case reporting, transmission modeling, and prognosis.
  • Highlighted research on social media sentiment analysis and knowledge discovery from scientific literature, all leveraging open-source resources.

Conclusions:

  • Open-source data and code are vital for validating, extending, and collaborating on COVID-19 research.
  • The surveyed open-source initiatives demonstrate the potential of digital technologies in addressing the pandemic.
  • Future research should prioritize open, extensible, and transparent data-driven approaches to collectively fight the global COVID-19 crisis.