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A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries.

Theyazn H H Aldhyani1, Hasan Alkahtani2

  • 1Applied College in Abqaiq, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia.

Life (Basel, Switzerland)
|November 27, 2021
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Summary
This summary is machine-generated.

This study developed a deep learning model to accurately predict COVID-19 active cases and deaths in Gulf countries. The bidirectional long short-term memory (Bi-LSTM) network achieved high accuracy, aiding pandemic control decisions.

Keywords:
Bi-LSTMCOVID-19Gulf countriesdeep learningtime series model

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Area of Science:

  • Epidemiology
  • Computational Biology
  • Artificial Intelligence

Background:

  • Accurate prediction of COVID-19 cases and deaths is crucial for pandemic management.
  • Existing models require enhancement for precise forecasting in specific regions.

Purpose of the Study:

  • To develop and evaluate an advanced prediction system for COVID-19 active cases and mortality.
  • To apply the bidirectional long short-term memory (Bi-LSTM) deep learning model to predict pandemic trends in Gulf countries.

Main Methods:

  • Utilized a deep learning approach, specifically the Bi-LSTM network.
  • Collected and analyzed COVID-19 case and death data from Saudi Arabia, Oman, UAE, Kuwait, Bahrain, and Qatar.
  • Employed statistical analyses including Mean Square Error, Root Mean Square Error, Mean Absolute Error, and Spearman's correlation coefficient for evaluation.

Main Results:

  • The Bi-LSTM model demonstrated high predictive accuracy for COVID-19 cases, with correlation metrics ranging from 98.95% to 99.94% across the studied Gulf countries.
  • Predictive accuracy for COVID-19 mortality using the Bi-LSTM model ranged from 95.62% to 99.87% in the investigated nations.
  • The model showed significant results, indicating its effectiveness and robustness in forecasting pandemic trends.

Conclusions:

  • The Bi-LSTM deep learning model is highly effective and robust for predicting COVID-19 active cases and deaths.
  • This advanced prediction system can significantly aid in decision-making for pandemic control in the Gulf region.
  • The study highlights the potential of deep learning in epidemiological forecasting.