Overview of Deep Learning-Based Contact Tracing Systems for Varied Population Dynamics and Pandemic Scenarios

Abstract

This research explores the critical dimensions of scalability and adaptability in the realm of deep learning-based contact tracing systems, aiming to fortify their robustness and flexibility across diverse population dynamics and pandemic scenarios. In the face of evolving health crises, the need for a contact tracing infrastructure capable of accommodating varying population sizes and dynamic pandemic conditions becomes imperative. Leveraging advanced deep learning techniques, our study focuses on enhancing the system's ability to scale seamlessly and adapt to different pandemic scenarios, including emerging infectious diseases and varying transmission dynamics. Through rigorous evaluation and refinement of the contact tracing architecture, we address challenges associated with scalability, ensuring the system's efficiency in handling large and dynamic populations. Additionally, the research contributes to the adaptability of deep learning models, enabling them to respond effectively to evolving pandemic scenarios. The findings underscore the importance of a flexible and robust deep learning-based contact tracing system, shedding light on its potential to significantly impact public health preparedness and response.