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

TL;DR

This paper introduces a new privacy-preserving app framework that proactively alerts users to reduce contacts before they become high-risk, potentially reducing the spread of diseases like COVID-19.

Contribution

NPCT introduces a proactive, graded alert system that reduces epidemic peaks while preserving privacy and minimizing social disruption.

Findings

NPCT simulations show a 40% reduction in epidemic peaks with only 20% contact suppression.

The intervention burden is concentrated on high-risk individuals.

NPCT's behavior remains stable across different network types and compliance levels.

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 ≈ 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.

During the COVID-19 pandemic, many countries adopted smartphone exposure-notification apps. These tools follow a binary rule: if a user’s cumulative exposure passes a fixed threshold, everyone involved is told to self-isolate. We noted two drawbacks: warnings come only after a positive test, and they can confine large numbers of people who pose little actual risk. To address this, we devised Network-based Proactive Contact Tracing (NPCT), a fully decentralized scheme that fits inside the privacy guardrails of existing apps. Each phone converts its owner’s recent Bluetooth encounters into a single risk score and compares that score with a threshold that tightens when case numbers rise and relaxes when they fall. Crossing the threshold triggers a request to trim only a chosen share of forthcoming contacts (for example, 25%) instead of imposing a blanket quarantine. We assessed NPCT through epidemic simulations on several synthetic and empirical temporal contact networks. The results show that this type of intervention can reduce the epidemic peak by roughly 40% while removing only one fifth of social interactions. NPCT therefore offers a realistic, privacy-preserving upgrade path for national exposure-notification systems.

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