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Updated: Aug 12, 2025

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
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Data-driven analysis and predictive modeling on COVID-19.

Sonam Sharma1, Izzat Alsmadi2, Rami S Alkhawaldeh3

  • 1Department of Electrical Engineering and Computer Science Syracuse University Syracuse USA.

Concurrency and Computation : Practice & Experience
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Summary
This summary is machine-generated.

This study analyzed the COVID-19 pandemic

Keywords:
COVID‐19gender of patientsglobal growth ratepredictive modelingsocial distancing

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Area of Science:

  • Epidemiology
  • Data Science
  • Machine Learning

Background:

  • The COVID-19 pandemic has caused widespread global impact since 2019.
  • Understanding demographic and intervention effects is crucial for pandemic management.

Purpose of the Study:

  • To develop a data-driven analytical model for COVID-19.
  • To investigate the pandemic's impact on different genders and age groups.
  • To assess the effectiveness of safety measures on virus transmission rates.

Main Methods:

  • Utilized machine learning and ensemble models for prediction.
  • Analyzed three key aspects: patient gender, global growth rate, and social distancing.
  • Employed classic classifiers, bagging, feature-based ensembles, voting, and stacking.

Main Results:

  • Demonstrated superior prediction performance compared to existing methods.
  • Validated models on three extensive public datasets.
  • Identified significant patterns in gender-specific impacts and intervention effectiveness.

Conclusions:

  • The proposed machine learning model offers robust analytical capabilities for COVID-19.
  • The findings provide insights into pandemic dynamics and control strategies.
  • Data-driven approaches are effective for understanding and predicting infectious disease spread.