  When pandemics like COVID-19 spread around the world, the rapidly evolving
situation compels officials and executives to take prompt decisions and adapt
policies depending on the current state of the disease. In this context, it is
crucial for policymakers to have always a firm grasp on what is the current
state of the pandemic, and to envision how the number of infections and
possible deaths is going to evolve over the next weeks. However, as in many
other situations involving compulsory registration of sensitive data from
multiple collectors, cases might be reported with errors, often with delays
deferring an up-to-date view of the state of things. Errors in collecting new
cases affect the overall mortality, resulting in excess deaths reported by
official statistics only months later. In this paper, we provide tools for
evaluating the quality of pandemic mortality data. We accomplish this through a
Bayesian approach accounting for the excess mortality pandemics might bring
with respect to the normal level of mortality in the population.
