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

TL;DR

This paper tackles how risk-contact data from digital contact monitoring (DCM) apps can inform infectious-disease transmission models. It proposes a simple SEIR framework that initializes the infectious compartment using an infectious-contact rate $c^I(t)$ derived from aggregated DCM data, with $I(t) = N \frac{c^I(t)}{c(t)}$ and the transmission rate $\beta(t) = \varepsilon c(t)$, enabling direct estimation of the instantaneous reproduction number $\mathcal{R}(t)$ without full model calibration. The authors implement two data pathways using COVID RADAR and CoronaMelder data, including a deconvolution step to convert notification timing into infection timing, and compare the resulting $\mathcal{R}(t)$ and infectious-population trajectories with RIVM benchmarks; findings show broad agreement (within ~25%) and a plausible capture of major waves, suggesting DCM data can provide timely population-level epidemic state signals when detailed data are scarce. They also discuss data accuracy and representativeness, and offer concrete recommendations to improve DCM data collection (e.g., dating infected contacts, reporting daily contact counts) to enhance real-time epidemic monitoring. Overall, the work demonstrates that coarse, privacy-preserving DCM data, when properly integrated, can yield useful, near real-time epidemic indicators and complement traditional surveillance sources.

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.

Figures (5)

• Figure 1: Relative difference between our estimates of the effective reproduction number ($\mathcal{R}^2(t) / \mathcal{R}^1(t) - 1$).
• Figure 2: Estimates of the effective reproduction number.
• Figure 3: Ratios between our effective reproduction number estimates and the estimate of RIVM.
• Figure 4: Estimates of the number of infectious individuals, including the 95% confidence interval for the estimate of RIVM.
• Figure 5: Ratios between our estimates of the number of infectious individuals and the estimate of RIVM.