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Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
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This study developed reliable artificial neural network and maximum likelihood estimation models to predict COVID-19 mortality rates in Italy. Accurate survival analysis aids in containing the COVID-19 pandemic.

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Area of Science:

  • Epidemiology
  • Medical Statistics
  • Computational Biology

Background:

  • The COVID-19 pandemic poses a significant global health challenge, necessitating accurate mortality prediction for effective containment.
  • Predicting survival outcomes for COVID-19 patients is crucial for public health interventions and resource allocation.
  • Artificial neural networks demonstrate success in medical prognosis and survival analysis.

Purpose of the Study:

  • To develop and compare two statistical models for estimating COVID-19 mortality rates in Italy.
  • To assess the reliability of artificial neural network modeling and maximum likelihood estimation for COVID-19 survival analysis.
  • To provide a tool for predicting COVID-19 mortality to aid epidemic containment strategies.

Main Methods:

  • Development of a multilayer artificial neural network model with 9 hidden neurons.
  • Application of maximum likelihood estimation for comparative analysis.
  • Validation of model predictions against numerical results.

Main Results:

  • The artificial neural network model achieved a maximum deviation of -0.14%.
  • The artificial neural network model demonstrated a high R value of 0.99836.
  • Both developed statistical models exhibited high reliability in estimating COVID-19 mortality.

Conclusions:

  • Artificial neural networks and maximum likelihood estimation are reliable methods for COVID-19 mortality prediction.
  • Accurate mortality prediction models can significantly aid in controlling the COVID-19 epidemic.
  • The study confirms the high reliability of the proposed statistical models for epidemiological analysis.