Three-Tier Indirect Tracing Model for Enhancing Epidemic Surveillance

Abstract

In the wake of recent global health crises, effective contact tracing has emerged as a key tool in controlling infectious disease outbreaks. However, traditional contact tracing methods predominantly focus on direct tracing, often overlooking crucial indirect contacts. This study aims to address this gap by exploring scenarios where conventional tracing fails to identify all potential contacts. We argue for the necessity of indirect tracing, a component typically absent in traditional schemes, and demonstrate its importance across different stakeholders: end users, service providers, and healthcare professionals. To this end, we have designed an end-to-end application, available on GitHub, which significantly enhances the efficacy of contact tracing. Our approach effectively doubles or triples the maximum number of traceable individuals compared to traditional direct contact tracing methods, thereby offering a more comprehensive and effective tool for epidemic surveillance and control. This may lead to significant improvements in contact-tracing applications, thereby containing virus outbreaks more efficiently. In addition to the comprehensive analysis and development of the robust architecture, this study also emphasizes the broader implications and potential impact of incorporating indirect tracing into contact tracing efforts.