Mitigating COVID-19 Transmission: A Machine Learning Approach to Contact Tracing Optimization

Abstract

In the battle against the COVID-19 pandemic, effective contact tracing plays a pivotal role in controlling transmission. Traditional contact tracing methods are resource-intensive and may not keep pace with the rapidly evolving nature of the virus. This paper proposes a novel approach to optimize contact tracing using machine learning (ML) algorithms. By leveraging ML techniques, we aim to enhance the accuracy and efficiency of contact tracing efforts, thereby reducing transmission rates. Our proposed framework integrates real-time data streams, including geographical, demographic, and epidemiological information, to identify and prioritize individuals at higher risk of exposure. Through predictive modeling and network analysis, we can optimize resource allocation for testing and quarantine measures. Additionally, our approach facilitates early detection of potential outbreaks and enables targeted interventions to contain the spread of the virus. We present a case study demonstrating the feasibility and effectiveness of our ML-based contact tracing system in a simulated pandemic scenario. The results underscore the potential of machine learning in augmenting public health strategies to combat COVID-19 and future infectious diseases.