  Contact tracing is one of the most important tools for preventing the spread
of infectious diseases, but as the experience of COVID-19 showed, it is also
next-to-impossible to implement when the disease is spreading rapidly. We show
how to substantially improve the efficiency of contact tracing by combining
standard microeconomic tools that measure heterogeneity in how infectious a
sick person is with ideas from machine learning about sequential optimization.
Our contributions are twofold. First, we incorporate heterogeneity in
individual infectiousness in a multi-armed bandit to establish optimal
algorithms. At the heart of this strategy is a focus on learning. In the
typical conceptualization of contact tracing, contacts of an infected person
are tested to find more infections. Under a learning-first framework, however,
contacts of infected persons are tested to ascertain whether the infected
person is likely to be a "high infector" and to find additional infections only
if it is likely to be highly fruitful. Second, we demonstrate using three
administrative contact tracing datasets from India and Pakistan during COVID-19
that this strategy improves efficiency. Using our algorithm, we find 80% of
infections with just 40% of contacts while current approaches test twice as
many contacts to identify the same number of infections. We further show that a
simple strategy that can be easily implemented in the field performs at nearly
optimal levels, allowing for, what we call, feasible contact tracing. These
results are immediately transferable to contact tracing in any epidemic.
