  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.
