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Outbreak prediction of COVID-19 using Recurrent neural network with Gated Recurrent Units

Sathish Natarajan1, Mohit Kumar2, Sai Kiran Kumar Gadde3

  • 1Cognizant Technology Solutions, Chennai, India.

Materials Today. Proceedings
|July 26, 2021
PubMed

Insights

This study introduces a Gated Recurrent Unit (GRU) model for predicting Coronavirus Disease 2019 (COVID-19) cases, offering an improvement over traditional Recurrent Neural Networks (RNNs). The model tracks infections, cures, and deaths to forecast the pandemic's impact.

Area of Science:

  • * Computer Science
  • * Epidemiology
  • * Public Health

Background:

  • * Coronavirus Disease 2019 (COVID-19), caused by SARS-CoV-2, emerged in late 2019 and rapidly became a global pandemic.
  • * Traditional Recurrent Neural Networks (RNNs), including Long Short-Term Memory (LSTM) networks, face limitations in addressing complex time-series prediction challenges.
  • * Accurate prediction of infection, recovery, and mortality rates is crucial for effective public health response and resource allocation.

Purpose of the Study:

  • * To propose and implement an enhanced Recurrent Neural Network (RNN) model utilizing Gated Recurrent Units (GRUs) for COVID-19 prediction.
  • * To develop a system for tracking and forecasting confirmed cases, cures, and deaths related to the COVID-19 pandemic.
  • * To assess the model's performance in predicting key COVID-19 metrics across multiple countries.

Main Methods:

  • * An ensemble approach combining RNN and Gated Recurrent Unit (GRU) architectures was implemented.
  • * The system was designed to monitor daily confirmed cases, reported cures, and deaths.
  • * Root Mean Square Error (RMSE) was calculated to evaluate prediction accuracy for infected, cured, and deceased cases.

Main Results:

  • * The implemented RNN-GRU ensemble model demonstrated the ability to compute RMSE values for infected, cure, and death cases.
  • * Performance was evaluated across four distinct countries: the Czech Republic, the United States, India, and Russia.
  • * The model provides a quantitative measure of prediction accuracy for key COVID-19 epidemiological indicators.

Conclusions:

  • * The proposed RNN with GRUs offers a viable approach for COVID-19 prediction, potentially overcoming limitations of standard RNNs.
  • * The developed system effectively tracks and analyzes pandemic data, aiding nations in understanding and managing the early stages of the outbreak.
  • * The RMSE values obtained provide insights into the model's predictive accuracy for crucial COVID-19 outcomes across diverse national contexts.

Keywords:
Deep learningLSTM GRURMSERecurrent neural network

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