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Novel Prediction Model for COVID-19 in Saudi Arabia Based on an LSTM Algorithm
Eman H Alkhammash1, Haneen Algethami1, Reem Alshahrani1
1Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
Computational Intelligence and Neuroscience
|December 22, 2021
Summary
Artificial intelligence, specifically long short-term memory (LSTM) models, accurately predicted COVID-19 cases. This research aids in understanding virus transmission and effective containment strategies for future pandemics.
Area of Science:
- Epidemiology
- Artificial Intelligence
- Public Health
Background:
- The novel SARS-CoV-2 pandemic presented a significant global health challenge.
- Artificial intelligence (AI) offers powerful tools for combating pandemics and controlling virus spread.
- Deep learning time-series techniques are crucial for predicting disease outbreaks like COVID-19.
Purpose of the Study:
- To predict daily COVID-19 cases using AI.
- To identify key factors influencing SARS-CoV-2 transmission rates.
- To analyze the effectiveness of COVID-19 prevention measures.
Main Methods:
- Employed deep learning models: fully connected neural networks, long short-term memory (LSTM), and transformer models.
- Utilized COVID-19 datasets from Saudi Arabia for data preprocessing and feature extraction.
- Performed comparative analysis of model performance metrics and statistical correlation analysis.
Main Results:
- The LSTM algorithm demonstrated the highest accuracy, achieving an error rate below 2%.
- Identified critical factors influencing COVID-19 transmission, including mobility and vaccination rates.
- Evaluated the effectiveness of various public health interventions.
Conclusions:
- AI, particularly LSTM, is a reliable tool for accurate COVID-19 case prediction.
- Findings enhance understanding of COVID-19 containment and inform future pandemic preparedness strategies.
- The study provides valuable insights for preventing and managing future infectious disease outbreaks.
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