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Area of Science:

  • Computational biology
  • Epidemiology
  • Artificial Intelligence in Medicine

Background:

  • The COVID-19 pandemic necessitated global scientific collaboration and flexible knowledge transmission.
  • Cognitive technologies and AI/ML experts were crucial for real-time data analysis and prediction.
  • Integrating diverse patient data for analysis presented significant challenges.

Purpose of the Study:

  • To perform in-depth Exploratory Data Analysis (EDA) on global COVID-19 medical data.
  • To leverage AI and machine learning for predictive modeling of the pandemic.
  • To facilitate future biomedical research on COVID-19 and associated pandemics.

Main Methods:

  • Utilized real-time global COVID-19 patient data.
  • Applied artificial intelligence and machine learning techniques for data tracking and prediction.
  • Conducted in-depth Exploratory Data Analysis (EDA) on complex medical datasets.

Main Results:

  • Successfully tracked and predicted trends using real-time pandemic data.
  • Demonstrated the relevance of physiological and clinical features for analysis.
  • Identified complexities in data transformation for predictive modeling.

Conclusions:

  • The study provides a foundation for advanced predictive and analytical research in virology.
  • Global data integration and AI/ML are vital for understanding and combating pandemics.
  • Findings will benefit future biomedical research on COVID-19 and related infectious diseases.