# Risk score learning for COVID-19 contact tracing apps

arXiv: https://arxiv.org/abs/2104.08415 · HTML: https://mlsanity.com/paper/2104.08415/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 17 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [CRISP: A Probabilistic Model for Individual-Level COVID-19 Infection Risk Estimation Based on Contact Data](https://mlsanity.com/paper/2006.04942/) (2020)
- [Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy Measurements](https://mlsanity.com/paper/2004.11841/) (2020)
- [Risk scoring calculation for the current NHSx contact tracing app](https://mlsanity.com/paper/2005.11057/) (2020)
- [Inferring proximity from Bluetooth Low Energy RSSI with Unscented Kalman Smoothers](https://mlsanity.com/paper/2007.05057/) (2020)

## Built on by (most cited first, 1 of 1)

- [Effectiveness of probabilistic contact tracing in epidemic containment: the role of super-spreaders and transmission path reconstruction](https://mlsanity.com/paper/2312.00910/) (2023)

## Abstract (arXiv)

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.