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Steps in Outbreak Investigation

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Residuals and Least-Squares Property

Residuals and Least-Squares Property

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Related Experiment Video

Updated: Dec 1, 2025

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia.

Ammar H Elsheikh¹, Amal I Saba², Mohamed Abd Elaziz³

¹Department of Production Engineering and Mechanical Design, Faculty of Engineering, Tanta University, Tanta, 31527, Egypt.

Process Safety and Environmental Protection : Transactions of the Institution of Chemical Engineers, Part B

|November 9, 2020

Summary

This study introduces a deep learning model, Long Short-Term Memory (LSTM) network, for accurate COVID-19 case, recovery, and death forecasting. The LSTM model demonstrated reasonable accuracy, outperforming other models in predicting pandemic trends.

Keywords:

COVID-19 Deep learning Forecasting Saudi Arabia

Related Experiment Videos

Last Updated: Dec 1, 2025

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

Area of Science:

Epidemiology
Deep Learning
Artificial Intelligence

Background:

The COVID-19 pandemic necessitates accurate forecasting for effective control strategies.
Predicting epidemiological behavior is crucial for managing the global spread of infectious diseases.

Purpose of the Study:

To propose and evaluate a Long Short-Term Memory (LSTM) network for forecasting COVID-19 confirmed cases, recovered cases, and deaths.
To assess the forecasting accuracy of the LSTM model using multiple statistical criteria.
To compare the LSTM model's performance against Autoregressive Integrated Moving Average (ARIMA) and Nonlinear Autoregressive Artificial Neural Networks (NARANN) models.

Main Methods:

Utilized official reported COVID-19 data for training the LSTM model.
Optimized LSTM model parameters to maximize forecasting accuracy.
Employed seven statistical assessment criteria (RMSE, R², MAE, EC, OI, COV, CRM) to evaluate model performance.
Compared LSTM predictions with ARIMA and NARANN models.
Applied the validated LSTM model to forecast COVID-19 trends in six diverse countries.

Main Results:

The LSTM model achieved reasonable forecasting accuracy for COVID-19 epidemiological data.
The proposed LSTM model demonstrated superior or comparable performance when benchmarked against ARIMA and NARANN models.
Forecasting was successfully extended to predict confirmed cases and deaths in Brazil, India, Saudi Arabia, South Africa, Spain, and the USA.

Conclusions:

Deep learning models like LSTM offer a robust approach for COVID-19 epidemiological forecasting.
The findings provide valuable insights for policymakers in disease control and strategic planning.
The study highlights the potential of AI in managing public health crises and informing decisions on social measures.