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 ≈ 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. Author summary 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 guard-rails 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% percent while removing only one fifth of social interactions. NPCT therefore offers a realistic, privacy-preserving upgrade path for national exposure-notification systems. Competing Interest Statement The authors have declared no competing interest. Funding Statement The author(s) received no specific funding for this work. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study did not involve new data collection from human participants. All data were either synthetic (ABM/ABM30) or publicly available and fully de-identified (DTU, Office). No IRB review or exemption was required. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability All contact-network datasets used in this study are publicly available. The Office network contains face-to-face proximity data collected in a French workplace and is available from the SocioPatterns repository at https://www.sociopatterns.org/datasets/office-proximity-network/. The DTU network represents the Bluetooth interaction layer of the Copenhagen Networks Study and can be accessed via Figshare at https://figshare.com/articles/dataset/The_Copenhagen_Networks_Study_interaction_data/7267433. The ABM network is available on Zenodo at https://doi.org/10.5281/zenodo.15076221. The ABM30 network is a 30-day extension of the ABM network and can be found at https://doi.org/10.5281/zenodo.15877149.