  Digital contact tracing aims to curb epidemics by identifying and mitigating
public health emergencies through technology. Backward contact tracing, which
tracks the sources of infection, proved crucial in places like Japan for
identifying COVID-19 infections from superspreading events. This paper presents
a novel perspective of digital contact tracing as online graph exploration and
addresses the forward and backward contact tracing problem as a
maximum-likelihood (ML) estimation problem using iterative epidemic network
data sampling. The challenge lies in the combinatorial complexity and rapid
spread of infections. We introduce DeepTrace, an algorithm based on a Graph
Neural Network (GNN) that iteratively updates its estimations as new contact
tracing data is collected, learning to optimize the maximum likelihood
estimation by utilizing topological features to accelerate learning and improve
convergence. The contact tracing process combines either BFS or DFS to expand
the network and trace the infection source, ensuring comprehensive and
efficient exploration. Additionally, the GNN model is fine-tuned through a
two-phase approach: pre-training with synthetic networks to approximate
likelihood probabilities and fine-tuning with high-quality data to refine the
model. Using COVID-19 variant data, we illustrate that DeepTrace surpasses
current methods in identifying superspreaders, providing a robust basis for a
scalable digital contact tracing strategy.
