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AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods
Muhammad Usman Tariq1,2, Shuhaida Binti Ismail2
1Marketing, Operations, and Information System, Abu Dhabi University, Abu Dhabi, United Arab Emirates.
Osong Public Health and Research Perspectives
|April 15, 2024
Summary
The Recurrent Neural Network (RNN) model demonstrated superior accuracy in forecasting COVID-19 cases in the UAE. This deep learning approach offers valuable insights for public health decision-making and targeted interventions.
Area of Science:
- Epidemiology
- Artificial Intelligence
- Public Health
Background:
- The COVID-19 pandemic presents ongoing public health challenges globally and in the UAE.
- Accurate forecasting of COVID-19 cases is crucial for effective public health management.
Purpose of the Study:
- To evaluate the efficiency and accuracy of deep learning models for COVID-19 case forecasting in the UAE.
- To support UAE public health authorities with data-driven decision-making tools.
Main Methods:
- Utilized a dataset of COVID-19 cases, demographics, and socioeconomic indicators.
- Trained and evaluated multiple deep learning models: LSTM, bidirectional LSTM, CNN, CNN-LSTM, MLP, and RNN.
- Employed Bayesian optimization for model fine-tuning.
Main Results:
- Different deep learning models showed varying predictive accuracy and precision.
- The RNN model achieved the highest performance among the evaluated architectures, even before optimization.
- Detailed predictive and perspective analytics were performed on the COVID-19 data.
Conclusions:
- The study provides critical insights for targeted, data-driven public health interventions in the UAE.
- The RNN model is identified as the most reliable for COVID-19 forecasting in this context, influencing public health strategies.
- Deep learning techniques demonstrate significant potential for enhancing predictive accuracy in public health and healthcare sectors.
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