When dealing with pandemics like COVID-19, it is crucial for policymakers to constantly monitor the emergency. Correct data reporting is a hard task during pandemics, and errors affect the overall mortality, resulting in excess deaths in official statistics. In this work, we provide tools for evaluating the quality of pandemic mortality data. We accomplish this through a spatio-temporal Bayesian approach accounting for the bias implicitly contained in the data
