Risk score learning for COVID-19 contact tracing apps

TL;DR

This paper presents a method to automatically learn risk score parameters for COVID-19 contact tracing apps using data, improving adaptability and performance over manual models, despite challenges like missing data and model misspecification.

Contribution

The paper introduces a data-driven approach to optimize risk score parameters for contact tracing, enhancing accuracy and adaptability over traditional manual methods.

Findings

Learning outperforms manual baseline models.

Estimation becomes harder with more missing data.

Method adapts to changing risk factors.

Abstract

Digital contact tracing apps for COVID, such as the one developed by Google and Apple, need to estimate the risk that a user was infected during a particular exposure, in order to decide whether to notify the user to take precautions, such as entering into quarantine, or requesting a test. Such risk score models contain numerous parameters that must be set by the public health authority. In this paper, we show how to automatically learn these parameters from data. Our method needs access to exposure and outcome data. Although this data is already being collected (in an aggregated, privacy-preserving way) by several health authorities, in this paper we limit ourselves to simulated data, so that we can systematically study the different factors that affect the feasibility of the approach. In particular, we show that the parameters become harder to estimate when there is more missing data (e.g., due to infections which were not recorded by the app), and when there is model misspecification. Nevertheless, the learning approach outperforms a strong manually designed baseline. Furthermore, the learning approach can adapt even when the risk factors of the disease change, e.g., due to the evolution of new variants, or the adoption of vaccines.