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Leveraging Data Science to Combat COVID-19: A Comprehensive Review.

Siddique Latif1,2, Muhammad Usman3,4, Sanaullah Manzoor4

  • 1University of Southern Queensland Springfield Queensland 4300 Australia.

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|November 20, 2023
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Summary
This summary is machine-generated.

This review systematizes data science applications for COVID-19 research, covering AI, machine learning, and data visualization. It identifies key datasets, analyzes research trends, and highlights challenges in combating the pandemic.

Keywords:
Bibliometric analysisCOVID-19SARS-CoV-2data sciencemachine learningmedical image analysisspeech analysistext mining

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Area of Science:

  • Data Science
  • Infectious Disease Research
  • Public Health

Background:

  • The COVID-19 pandemic, caused by SARS-CoV-2, rapidly spread globally starting in March 2020.
  • Over 21 million cases were reported worldwide by mid-August 2020, necessitating urgent research efforts.
  • Data science offers powerful tools for understanding and mitigating the pandemic's impact.

Purpose of the Study:

  • To systematically review and categorize COVID-19 research utilizing data science methods.
  • To survey available public datasets and repositories for tracking disease spread and mitigation.
  • To provide a bibliometric analysis of early COVID-19 data science research and identify common challenges.

Main Methods:

  • Broad definition of data science, including artificial intelligence (AI), machine learning (ML), statistics, modeling, simulation, and data visualization.
  • Comprehensive literature review of recent COVID-19 research leveraging data science.
  • Survey of public datasets and repositories relevant to COVID-19.
  • Bibliometric analysis of published research papers.
  • Identification and discussion of common challenges and pitfalls.

Main Results:

  • A systematic overview of diverse data science applications in COVID-19 research.
  • Identification of key public datasets and repositories for epidemiological tracking and intervention analysis.
  • A bibliometric analysis revealing the rapid growth and focus areas of COVID-19 data science research.
  • A curated list of common challenges and potential pitfalls in applying data science to pandemic response.

Conclusions:

  • Data science plays a crucial role in understanding, tracking, and combating the COVID-19 pandemic.
  • Accessible datasets and robust data science methodologies are vital for effective public health responses.
  • Continued development and sharing of resources, like the live repository provided, are essential for ongoing research and mitigation efforts.