Unraveling COVID-19 Progression: A Comprehensive Com-parison of Advanced Deep Learning Methods for Precise Pre-dictions

The COVID-19 pandemic has significantly impacted the United Arab Emirates (UAE), necessitating effective and accurate forecasting tools to inform public health policies and strategies. This study presents a comparative analysis of advanced deep-learning models for predicting COVID-19 cases in the UAE. We investigate the performance of Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Networks (CNN), CNN-LSTM, Multilayer perceptron, and Recurrent Neural Networks (RNN). The models are trained and evaluated using a comprehensive dataset of confirmed cases, demographic information, and relevant socio-economic indicators. The models are further optimized using a Bayesian optimizer and comparison is performed before and after the optimization of models. We have used predictive and perspective analytics on the COVID-19 dataset. Our research goal is to identify the most accurate and reliable model for forecasting COVID-19 cases in the region. The results demonstrate the effectiveness of these deep learning techniques in predicting COVID-19 cases, with each model exhibiting varying levels of accuracy and precision. A thorough and rigorous evaluation of the models' performances reveals the most suitable architecture for the UAE's specific context. This study contributes to the ongoing efforts to combat the pandemic by providing valuable insights into the application of advanced deep-learning models for accurate and timely COVID-19 case predictions. It was found that the RNN model performed the best without any optimization. The findings have significant implications for public health decision-making, enabling authorities to develop targeted and data-driven interventions to curb the spread of the virus and mitigate its impact on the UAE's population. This demonstrates the potential of deep learning algorithms in handling complex datasets and making accurate predictions which is a valuable capability to enhance accuracy in professional and healthcare environments.