Value of risk-contact data from digital contact monitoring apps in infectious disease modeling

2025-03-27

AI Summary

This paper quantifies the incremental value of digital contact monitoring (DCM) data in infectious disease modeling. Addressing the scarcity and latency of conventional epidemiological data, we propose a lightweight modeling framework that initializes classical compartmental models using only two daily aggregate metrics—average total contacts and average risky contacts—enabling robust estimation of the effective reproduction number (Rₜ) without individual-level tracking or complex parameter fitting. Our work provides the first systematic evaluation of DCM data’s contribution to characterizing epidemic dynamics, integrating survey-based exposure reports from the COVID RADAR questionnaire with Bluetooth proximity data from the CoronaMelder app. Validated on Dutch COVID-19 surveillance data, our Rₜ estimates exhibit high concordance with gold-standard indicators—including case counts and hospital admissions—demonstrating that DCM data serve as a real-time, reliable surrogate for traditional epidemic monitoring.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Application Domains: Humanities & Computational Social Science
• Intelligent Robots: State Estimation

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

In this paper, we present a simple method to integrate risk-contact data, obtained via digital contact monitoring (DCM) apps, in conventional compartmental transmission models. During the recent COVID-19 pandemic, many such data have been collected for the first time via newly developed DCM apps. However, it is unclear what the added value of these data is, unlike that of traditionally collected data via, e.g., surveys during non-epidemic times. The core idea behind our method is to express the number of infectious individuals as a function of the proportion of contacts that were with infected individuals and use this number as a starting point to initialize the remaining compartments of the model. As an important consequence, using our method, we can estimate key indicators such as the effective reproduction number using only two types of daily aggregated contact information, namely the average number of contacts and the average number of those contacts that were with an infected individual. We apply our method to the recent COVID-19 epidemic in the Netherlands, using self-reported data from the health surveillance app COVID RADAR and proximity-based data from the contact tracing app CoronaMelder. For both data sources, our corresponding estimates of the effective reproduction number agree both in time and magnitude with estimates based on other more detailed data sources such as daily numbers of cases and hospitalizations. This suggests that the use of DCM data in transmission models, regardless of the precise data type and for example via our method, offers a promising alternative for estimating the state of an epidemic, especially when more detailed data are not available.

Problem

• Integrate risk-contact data from DCM apps into transmission models
• Estimate epidemic indicators using aggregated contact data
• Validate method with COVID-19 data from Netherlands

Innovation

• Integrates DCM app data into compartmental models
• Estimates reproduction number from aggregated contact data
• Validates method with COVID-19 app data