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Analysis on novel coronavirus (COVID-19) using machine learning methods.

Milind Yadav1, Murukessan Perumal2, M Srinivas2

  • 1Rajasthan Technical University, Kota, India.

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|August 25, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Support Vector Regression method to analyze and predict the spread, growth, and ending of the COVID-19 pandemic. The approach offers superior efficiency and accuracy in understanding virus transmission and mitigation effectiveness.

Keywords:
Active casesCOVID-19Novel coronavirusPearsonPolynomial regressionRecoveriesSimple linear regressionSupport vector regression model

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Area of Science:

  • Epidemiology
  • Infectious Disease Modeling
  • Data Science

Background:

  • The COVID-19 pandemic caused severe global health and economic disruption.
  • Understanding transmission dynamics and predicting epidemic trajectories are crucial for effective public health responses.

Purpose of the Study:

  • To predict the spread and ending of the novel coronavirus (COVID-19) pandemic.
  • To analyze growth rates, mitigation effectiveness, and transmission dynamics.
  • To correlate COVID-19 spread with weather conditions.

Main Methods:

  • A novel Support Vector Regression (SVR) method was developed.
  • The SVR approach utilizes support vectors for enhanced classification accuracy.
  • The proposed method was evaluated against established regression models.

Main Results:

  • The SVR method demonstrated superior efficiency and accuracy in analyzing COVID-19 related tasks.
  • The approach provides insights into virus spread, mitigation impact, and potential recovery.
  • Results indicate the model's effectiveness in predicting epidemic trends.

Conclusions:

  • The novel SVR method offers a robust tool for analyzing and predicting pandemic behavior.
  • Accurate prediction and analysis can inform public health strategies and resource allocation.
  • Understanding transmission and environmental factors aids in controlling the spread of infectious diseases.