  Digital contact tracing apps for COVID-19, 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. Although expert guidance for how to set these parameters has been
provided (e.g.
https://github.com/lfph/gaen-risk-scoring/blob/main/risk-scoring.md), it is
natural to ask if we could do better using a data-driven approach. This can be
particularly useful when the risk factors of the disease change, e.g., due to
the evolution of new variants, or the adoption of vaccines.
  In this paper, we show that machine learning methods can be used to
automatically optimize the parameters of the risk score model, provided we have
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). Nevertheless, the learning approach outperforms a strong
manually designed baseline.
