Deep learning architectures for influenza dynamics and treatment optimization: a comprehensive review

As a major worldwide health concern, influenza still requires precise modeling of flu dynamics and efficient treatment approaches. Deep learning architectures are increasingly being applied to address the complexities of influenza dynamics and treatment optimization, which remain critical global health challenges. This review explores the utilization of deep learning methods, such as Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), transformer architectures, and large language models (LLMs), in modeling influenza virus behavior and enhancing therapeutic strategies. The dynamic nature of influenza viruses, characterized by rapid mutation rates and the emergence of new strains, complicates the development of effective treatments and vaccines. In other words, the discovery of effective treatments and vaccines is severely hampered by the dynamic character of flu viruses, their fast rates of mutation, and the appearance of novel strains. Traditional epidemiological models often fall short due to their reliance on manual data interpretation and limited capacity to analyze large datasets. In contrast, deep learning offers a more automated and objective approach, capable of uncovering intricate patterns within extensive flu-related data, including genetic sequences and patient records. The application of deep learning to comprehend flu dynamics and improve treatment strategies is examined in this review paper. Moreover, this paper discussed relevant research findings, and future directions in leveraging deep learning for improved understanding and management of influenza outbreaks, ultimately aiming for more personalized treatment regimens and enhanced public health responses.