Deep learning-based tools for contact tracing in cases of viral infections

The outbreak of viral infections has led to governments and experts worldwide joining forces to find adequate measures and tactics for controlling and eventual community revival. With the progress in equipment capabilities and remote communications, the use of information-based technologies is becoming more and more important in identifying, detecting, and diagnosing possible cases of viral infections. This research seeks to investigate factors for early diagnosis, tracking, and identification of the spread of viral infections, with a focus on gathering information and investigating possibilities for improvement. The study acknowledges that deep learning models are suitable for lessening the influence of viral infections, with the wealth of pandemic information accessible via different technologies and cooperative efforts. While not yet widely used or proven in clinical settings, deep learning and big data methods offer quick responses and important information to medical staff and decision-makers. Nonetheless, developing deep learning algorithms for viral infections poses several challenges. The quality and quantity of viral infections datasets require further enhancement, necessitating continuous efforts from the research community to improve data quality and reliability.