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Data analytics for novel coronavirus disease.

M Rubaiyat Hossain Mondal1, Subrato Bharati1, Prajoy Podder1

  • 1Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, 1205, Bangladesh.

Informatics in Medicine Unlocked
|August 25, 2020
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Summary
This summary is machine-generated.

This study analyzes the spread and diagnosis of coronavirus disease (COVID-19) using data analytics. Machine learning models accurately identified COVID-19 patients, highlighting data analytics

Keywords:
COVID-19ClassificationCoronavirusMachine learningRegressionSARS-CoV-2

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Area of Science:

  • Epidemiology
  • Data Science
  • Medical Informatics

Background:

  • Novel coronavirus disease (COVID-19) emerged in December 2019.
  • Understanding COVID-19's origin, transmission, and symptoms is crucial.
  • Data analytics offers potential for insights into viral infections.

Purpose of the Study:

  • To describe COVID-19 aspects and visualize infection spread.
  • To apply data analytics for understanding COVID-19 transmission and diagnosis.
  • To model the global increase in COVID-19 cases.

Main Methods:

  • Literature survey on COVID-19 characteristics.
  • Data analytics applied to Johns Hopkins University and Hospital Israelita Albert Einstein datasets.
  • Polynomial regression for modeling case increase; classification algorithms (MLP, XGBoost, logistic regression) for diagnosis.

Main Results:

  • The USA had the highest confirmed COVID-19 cases by June 04, 2020, despite originating in China.
  • Polynomial regression modeled the worldwide increase in cases.
  • Classification models achieved over 91% accuracy in diagnosing COVID-19.

Conclusions:

  • Data analytics is valuable for tracking COVID-19 spread and aiding diagnosis.
  • Machine learning models demonstrate high efficacy in identifying COVID-19.
  • Further applications of data analytics in managing COVID-19 are discussed.