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A deep learning-based approach for predicting COVID-19 diagnosis.

Raafat M Munshi¹, Mashael M Khayyat², Sami Ben Slama^3,4

¹Department of Medical Laboratory Technology (MLT) Faculty of Applied Medical Sciences, King Abdulaziz University, Rabigh, Saudi Arabia.

|April 10, 2024

Summary

This summary is machine-generated.

This study forecasts COVID-19 cases in Saudi Arabia using ARIMA, mathematical modeling, and deep learning networks (DQN). Deep learning networks (DQN) demonstrated superior accuracy and efficiency compared to traditional forecasting methods.

Keywords:

ARIMA Artificial intelligence Forecasting Machine learning Mathematical model Time series

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Area of Science:

Epidemiology
Computational Biology
Data Science

Background:

Accurate forecasting of COVID-19 cases is crucial for public health interventions.
Traditional methods like ARIMA and mathematical modeling have limitations in complex epidemic prediction.
Deep learning offers potential for enhanced accuracy in disease forecasting.

Purpose of the Study:

To forecast confirmed COVID-19 cases in Saudi Arabia.
To compare the predictive performance of ARIMA, mathematical modeling, and Deep Neural Networks (DQN).
To identify the most effective method for COVID-19 case prediction to inform public health strategies.

Main Methods:

Utilized Autoregressive Integrated Moving Average (ARIMA) models.
Employed mathematical modeling techniques for time-series forecasting.
Applied Deep Neural Network (DQN) algorithms for comparative prediction.
Data spanned COVID-19 cases in Saudi Arabia, UK, and Tunisia from 2020-2021.

Main Results:

Deep Neural Network (DQN) technology outperformed conventional ARIMA and mathematical modeling approaches.
DQN demonstrated higher efficiency and accuracy in predicting COVID-19 case counts.
Comparative analysis highlighted the strengths of deep learning in epidemic forecasting.

Conclusions:

Deep learning networks (DQN) represent a more reliable and accurate method for forecasting COVID-19 cases.
The findings support the adoption of advanced computational methods for public health preparedness.
Enhanced prediction capabilities can significantly aid in planning and executing effective interventions.