Published Paper Details:

Contact tracing is a critical tool in the fight against infectious diseases, including the ongoing COVID-19 pandemic. Traditional contact tracing methods involve manually identifying and notifying individuals who have come into contact with an infected person. However, with the increasing scale of outbreaks, manual contact tracing has become increasingly difficult and time-consuming. Machine learning can help automate and enhance the contact tracing process. In this context, ML algorithms can analyze large volumes of data to identify potential transmission chains, predict the likelihood of an individual being infected, and prioritize high-risk individuals for testing and isolation. This paper presents an overview of recent research on contact tracing using machine learning. The paper covers ML techniques and their applications that involve Clustering and DBSCAN Algorithms and Proximity Graph. Additionally, the paper discusses the challenges and ethical considerations associated with using ML in contact tracing, such as data privacy and bias. Overall, this paper highlights the potential of ML to improve the effectiveness and efficiency of contact tracing, ultimately helping to curb the spread of infectious diseases.