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Analyzing the Effect of Vaccination Over COVID Cases and Deaths in Asian Countries Using Machine Learning Models.

Vanshika Rustagi1, Monika Bajaj2, Tanvi3

  • 1Molecular Biology Research Lab., Department of Zoology, Deshbandhu College, University of Delhi, New Delhi, India.

Frontiers in Cellular and Infection Microbiology
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Summary

This study analyzes COVID-19 data to predict mortality rates based on vaccination status. Findings help understand vaccine effectiveness against the triple-mutated virus and inform public health strategies.

Keywords:
COVID-19OLS regressionSupport Vector Machine (SVM)linear regressionmachine learningpolynomial distribution

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Area of Science:

  • Epidemiology
  • Public Health
  • Biostatistics

Background:

  • Coronavirus Disease 2019 (COVID-19) presents a global health challenge, exacerbated by viral mutations.
  • Rapid person-to-person transmission occurs via contact and respiratory droplets.

Purpose of the Study:

  • To analyze COVID-19 data and predict infection and mortality rates.
  • To investigate the influence of vaccination doses on COVID-19 mortality fluctuations.

Main Methods:

  • Analysis of 'Our World in Data' from Feb 2020 to Sep 2021.
  • Linear regression and quartic polynomial regression models were employed.
  • Karl Pearson's coefficient and Support Vector Machines (SVM) were used for analysis.

Main Results:

  • Models were created to estimate COVID-19 deaths based on vaccination status (partial or complete).
  • Predictor models determine COVID-19 susceptibility relative to vaccine doses received.
  • SVM analyzed the efficacy of the developed predictive models.

Conclusions:

  • Vaccination status is a key factor in predicting COVID-19 mortality.
  • The developed models offer insights into disease susceptibility and outcomes.
  • Data-driven insights support public health interventions for COVID-19.