  Inferring the source of a diffusion in a large network of agents is a
difficult but feasible task, if a few agents act as sensors revealing the time
at which they got hit by the diffusion. A main limitation of current source
detection algorithms is that they assume full knowledge of the contact network,
which is rarely the case, especially for epidemics, where the source is called
patient zero. Inspired by recent contact tracing algorithms, we propose a new
framework, which we call Source Detection via Contact Tracing Framework
(SDCTF). In the SDCTF, the source detection task starts at the time of the
first hospitalization, and initially we have no knowledge about the contact
network other than the identity of the first hospitalized agent. We may then
explore the network by contact queries, and obtain symptom onset times by test
queries in an adaptive way. We also assume that some of the agents may be
asymptomatic, and therefore cannot reveal their symptom onset time. Our goal is
to find patient zero with as few contact and test queries as possible.
  We propose two local search algorithms for the SDCTF: the LS algorithm is
more data-efficient, but can fail to find the true source if many asymptomatic
agents are present, whereas the LS+ algorithm is more robust to asymptomatic
agents. By simulations we show that both LS and LS+ outperform state of the art
adaptive and non-adaptive source detection algorithms adapted to the SDCTF,
even though these baseline algorithms have full access to the contact network.
Extending the theory of random exponential trees, we analytically approximate
the probability of success of the LS/ LS+ algorithms, and we show that our
analytic results match the simulations. Finally, we benchmark our algorithms on
the Data-driven COVID-19 Simulator developed by Lorch et al., which is the
first time source detection algorithms are tested on such a complex dataset.
