Supplementary Material

Meghna Banerjee; Debashis Chatterjee; ARINDOM CHAKRABORTY

doi:10.5281/zenodo.18656389

February 16, 2026Open Access

Supplementary Material

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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.

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Implication

This repository compiles materials related to machine learning analysis of COVID-19 severity based on variants and demographics.

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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Banerjee et al. (2026) studied this question.

synapsesocial.com/papers/6996a818ecb39a600b3ee8b8 https://doi.org/10.5281/zenodo.18656389

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Supplementary Material

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