Tracing wide-spread infectious contacts using GeoSpark in preventive medicine.

The article focuses on a novel contact tracing query processing method designed to mitigate the spread of infectious diseases by analyzing users' movement trajectories based on exposure time and social proximity. Leveraging the GeoSpark extension of the Apache Spark framework, the method addresses scalability challenges inherent in processing large spatial datasets by partitioning and indexing trajectory data for efficient parallel querying. The approach distinguishes between close and proximate contacts and supports both direct (first-order) and indirect (iterative) tracing modes to identify high-risk and low-risk individuals during a disease's incubation period. Experimental results using large-scale synthetic datasets demonstrate that this Spark-based solution outperforms existing methods in memory usage and processing time while maintaining interactive query performance. This work contributes a scalable, data-driven tool for public health authorities and researchers aiming to enhance infectious disease containment through effective contact tracing.