Development of Pandemic Contact Tracing System using Convolutional Neural Network

Abstract

 The study presents a pioneering approach to pandemic management by harnessing Convolutional Neural Networks (CNN) to revolutionize contact tracing systems. The primary focus is on enhancing accuracy, efficiency, and adaptability within pandemic scenarios. The study's novelty lies in the strategic selection of CNN, a powerful deep learning methodology renowned for its intricate pattern recognition capabilities. This innovative choice transcends traditional techniques, introducing a flexible and adaptable system tailored for identifying potential contacts and exposures during pandemics. The study encompasses the complete lifecycle of the contact tracing system, from development and implementation to real-world evaluation. The system's successful implementation underscores its practicality and readiness for testing and deployment. The utilization of CNN provides a unique edge, as its inherent spatial feature recognition aptitude amplifies accuracy, significantly improving the system's precision. Through real-world validation, this study establishes the system's efficacy, substantiating the benefits of its adoption in pandemic response. In essence, this study paves the way for an advanced contact tracing paradigm, showcasing the potential of deep learning methodologies to elevate the accuracy, efficiency, and global impact of pandemic management strategies.