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Pandemic coronavirus disease (Covid-19): World effects analysis and prediction using machine-learning techniques.

Dimple Tiwari1, Bhoopesh Singh Bhati1, Fadi Al-Turjman2

  • 1Ambedkar Institute of Advanced Communication Technologies and Research, Govt of NCT of Delhi Delhi India.

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|June 28, 2021
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Summary

Artificial intelligence, specifically Naïve Bayes, effectively predicts Coronavirus Disease (COVID-19) trends using global case data. This approach offers a more accurate forecast than standard methods for pandemic response.

Keywords:
Covid‐19Naïve Bayesartificial intelligencedata analyticslinear regressionmachine‐learning predictionsupport vector machine

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Area of Science:

  • Epidemiology
  • Infectious Disease Modeling
  • Artificial Intelligence in Public Health

Background:

  • The novel Coronavirus (COVID-19) pandemic, originating in Wuhan, China, has had a significant global impact.
  • Existing predictive models struggle with data uncertainty and inaccuracies, hindering effective epidemic control.
  • Accurate forecasting is crucial for informed decision-making and implementing appropriate public health measures.

Purpose of the Study:

  • To develop and evaluate an Artificial Intelligence (AI)-based meta-analysis for predicting global COVID-19 epidemic trends.
  • To address the limitations of traditional methods in forecasting pandemic trajectories.
  • To provide insights for proactive governmental and citizen responses.

Main Methods:

  • Application of machine learning algorithms: Naïve Bayes, Support Vector Machine (SVM), and Linear Regression.
  • Utilized a real-time series dataset encompassing global confirmed, recovered, deaths, and active COVID-19 cases.
  • Conducted statistical analysis on symptoms, top affected countries, and co-active cases.

Main Results:

  • Naïve Bayes demonstrated superior performance in predicting COVID-19 future trends compared to SVM and Linear Regression.
  • Lower Mean Absolute Error (MAE) and Mean Squared Error (MSE) values for Naïve Bayes indicate its effectiveness.
  • Statistical analysis provided insights into disease patterns and country-specific impacts.

Conclusions:

  • AI-based meta-analysis, particularly using Naïve Bayes, offers a promising approach for accurate COVID-19 trend prediction.
  • The study establishes a benchmark for machine learning applications in outbreak forecasting.
  • Findings support proactive global responses to the ongoing pandemic.