  We present a novel Graph-based debiasing Algorithm for Underreported Data
(GRAUD) aiming at an efficient joint estimation of event counts and discovery
probabilities across spatial or graphical structures. This innovative method
provides a solution to problems seen in fields such as policing data and
COVID-$19$ data analysis. Our approach avoids the need for strong priors
typically associated with Bayesian frameworks. By leveraging the graph
structures on unknown variables $n$ and $p$, our method debiases the
under-report data and estimates the discovery probability at the same time. We
validate the effectiveness of our method through simulation experiments and
illustrate its practicality in one real-world application: police 911
calls-to-service data.
