The goal of digital contact tracing is to diminish the spread of an epidemic
or pandemic by detecting and mitigating public health emergencies using digital
technologies. Since the start of the COVID-19 pandemic, a wide variety of
mobile digital apps have been deployed to identify people exposed to the
SARS-CoV-2 coronavirus and to stop onward transmission. Tracing sources of
spreading (i.e., backward contact tracing), as has been used in Japan and
Australia, has proven crucial as going backwards can pick up infections that
might otherwise be missed at superspreading events. How should robust backward
contact tracing automated by mobile computing and network analytics be
designed? In this paper, we formulate the forward and backward contact tracing
problem for epidemic source inference as maximum-likelihood (ML) estimation
subject to subgraph sampling. Besides its restricted case (inspired by the
seminal work of Zaman and Shah in 2011) when the full infection topology is
known, the general problem is more challenging due to its sheer combinatorial
complexity, problem scale and the fact that the full infection topology is
rarely accurately known. We propose a Graph Neural Network (GNN) framework,
named DeepTrace, to compute the ML estimator by leveraging the likelihood
structure to configure the training set with topological features of smaller
epidemic networks as training sets. We demonstrate that the performance of our
GNN approach improves over prior heuristics in the literature and serves as a
basis to design robust contact tracing analytics to combat pandemics