Contact tracing stands as a crucial pillar in global infectious disease control, where innovative methodologies can significantly bolster its effectiveness. This study introduces a framework tailored for analyzing contact data within educational settings, focusing on interactions among 500 students and an infected individual. The resulting algorithm adeptly identifies potential encounters, empowering public health officials to assess transmission risks and optimize intervention strategies. Addressing privacy concerns, anonymized student data is utilized, emphasizing the delicate balance between public health priorities and individual privacy rights. Furthermore, this research illustrates the framework's adaptability to real-world scenarios through the integration of authentic location data and disease-specific parameters. Offering scalability and cost-effectiveness, this framework presents a blueprint for enhancing contact tracing strategies amidst evolving health crises, highlighting technology's transformative potential in infectious disease control.
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Kedia et al. (2024) studied this question.
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