Network-based proactive contact tracing: A pre-emptive, degree-based alerting framework for privacy-preserving COVID-19 apps

Abstract

Most COVID-19 exposure-notification apps still use binary contact tracing (BCT): once a test is positive, every contact whose accumulated risk exceeds a fixed threshold receives the same quarantine order. Because those alerts are late and blunt, BCT can miss early spread while triggering mass isolation. We propose Network-based Proactive Contact Tracing (NPCT), a privacy-preserving, fully decentralized intervention scheme that can run on existing exposure-notification infrastructure. Each user’s recent Bluetooth contact history is condensed into an individual risk score and compared against a dynamic, epidemic-aware threshold controlled by a single global sensitivity parameter. Crossing that threshold triggers a graded “reduce contacts by X%” prompt rather than an all-or-nothing quarantine. Simulations on four synthetic and empirical temporal networks show that NPCT can cut the epidemic peak by up to 40% while suppressing only 20% of contacts. The intervention burden concentrates on the highest-risk individuals, and the scheme’s qualitative behavior remains stable across network types, horizons, and compliance levels. These properties make NPCT a practical upgrade path for national BCT apps, balancing epidemic control with privacy protection and social cost.