Contact Tracing

Contact tracing aims to identify individuals who have been in close proximity to infected persons to curb disease transmission. Current research focuses on improving the accuracy and privacy of contact tracing methods, employing machine learning models (like gradient boosting and neural networks) to analyze data from various sources, including Bluetooth signals, inertial measurement units (IMUs), and Wi-Fi signals. These advancements aim to enhance the effectiveness of contact tracing, particularly in managing outbreaks, while addressing crucial privacy concerns through techniques like differential privacy. The development of more accurate and privacy-preserving contact tracing technologies has significant implications for public health and disease control.