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Synapse
February 19, 20260 citationsOpen Access

Supplementary Material

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MBMeghna BanerjeeBanasthali UniversityDCDebashis ChatterjeeVisva-Bharati UniversityACARINDOM CHAKRABORTYVisva-Bharati University

Key Points

  • The aim is to explore patterns in COVID-19 severity using machine learning techniques that integrate genomic and demographic factors.
  • Collection of genomic mutation data associated with recent COVID-19 variants.
  • Use of machine learning algorithms to analyze demographic information and severity trends.
  • Compilation of supplementary materials for a comprehensive understanding of methods and results.
  • Identified key genomic mutations linked to variations in COVID-19 severity.
  • Demographic factors demonstrated significant influence on COVID-19 outcomes.
  • Machine learning models improved prediction accuracy of severity based on variant data.

Abstract

This repository contains all supplementary material associated with the manuscript titled “Current Trends in COVID-19 Severity: A Machine Learning Approach Based on Recent Variants, Genomic Mutations and Demographic Information.”

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Cite This Study

Banerjee et al. (2026) studied this question.

synapsesocial.com/papers/6996a818ecb39a600b3ee8b8https://doi.org/10.5281/zenodo.18656389
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