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Related Experiment Video

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Artificial neural networks and statistical models for optimization studying COVID-19.

Azhari A Elhag1, Tahani A Aloafi1, Taghreed M Jawa1

  • 1Department of Mathematics and Statistics, College of Science, P.O. Box 11099, Taif University, Taif 21944, Saudi Arabia.

Results in Physics
|May 17, 2021
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Summary

This study evaluated the first COVID-19 peak using artificial neural networks and logistic regression. Statistical analysis of European countries data prepared for future pandemic waves.

Keywords:
Artificial neural networksCOVID-19DeathsLogistic regressionStatistical analysis

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Data Science

Background:

  • The COVID-19 pandemic presented a significant global challenge in 2020, causing mortality and economic disruption.
  • Assessing the initial phase of the pandemic is crucial for preparedness against subsequent waves.
  • Statistical evaluation of early pandemic data is necessary to understand transmission dynamics and inform public health strategies.

Purpose of the Study:

  • To statistically analyze the initial phase of the COVID-19 pandemic in European countries.
  • To evaluate the effectiveness of predictive models in understanding pandemic trends.
  • To provide insights for managing future waves of the coronavirus disease.

Main Methods:

  • Utilized World Health Organization (WHO) data for 32 European countries spanning January 11 to May 29, 2020.
  • Employed Artificial Neural Networks (ANNs) and Logistic Regression (LR) models for statistical analysis.
  • Extracted key statistical indicators to interpret pandemic characteristics.

Main Results:

  • Artificial Neural Networks demonstrated a classification accuracy rate of 85.6%.
  • Logistic Regression models achieved a classification accuracy rate of 80.8%.
  • The study identified significant statistical indicators relevant to the early COVID-19 pandemic.

Conclusions:

  • Both ANNs and LR models proved effective in analyzing early COVID-19 data.
  • The findings offer valuable statistical insights for pandemic preparedness and response.
  • Data-driven evaluation is essential for navigating the complexities of global health crises like COVID-19.