Objectives: The coronavirus disease 2019 (COVID-19) pandemic continues to pose significant challenges to the public health sector, including that of the United Arab Emirates (UAE). The objective of this study was to assess the efficiency and accuracy of various deep-learning models in forecasting COVID-19 cases within the UAE, thereby aiding the nation’s public health authorities in informed decision-making. Methods: This study utilized a comprehensive dataset encompassing confirmed COVID-19 cases, demographic statistics, and socioeconomic indicators. Several advanced deep learning models, including long short-term memory (LSTM), bidirectional LSTM, convolutional neural network (CNN), CNN-LSTM, multilayer perceptron, and recurrent neural network (RNN) models, were trained and evaluated. Bayesian optimization was also implemented to fine-tune these models. Results: The evaluation framework revealed that each model exhibited different levels of predictive accuracy and precision. Specifically, the RNN model outperformed the other architectures even without optimization. Comprehensive predictive and perspective analytics were conducted to scrutinize the COVID-19 dataset. Conclusion This study transcends academic boundaries by offering critical insights that enable public health authorities in the UAE to deploy targeted data-driven interventions. The RNN model, which was identified as the most reliable and accurate for this specific context, can significantly influence public health decisions. Moreover, the broader implications of this research validate the capability of deep learning techniques in handling complex datasets, thus offering the transformative potential for predictive accuracy in the public health and healthcare sectors.

Appendiceal neuroendocrine tumors (NET) although rare, but the most common tumors of the gastrointestinal tract in children and adolescents. NET of the appendix is typically undiagnosed preoperatively, are usually not associated with specific neuroendocrine symptoms, and a high percentage are initially identified by pathologists.For well-differentiated tumors of ＜1 cm and complete (R0) resection, no follow-up is required. Unlikely, tumor size ＞2 cm or tumor with high-risk features confer a relevant risk of recurrence and further imaging and surgical procedures are warranted.No consensus, clear recommendation or management guidelines are available for the management of appendiceal NET in children. Herein, current article will provide an overview of literature and suggested guidelines for evaluation and management of childhood neuroendocrine tumors of appendix.