  COVID-19 related deaths underestimate the pandemic burden on mortality
because they suffer from completeness and accuracy issues. Excess mortality is
a popular alternative, as it compares observed with expected deaths based on
the assumption that the pandemic did not occur. Expected deaths had the
pandemic not occurred depend on population trends, temperature, and
spatio-temporal patterns. In addition to this, high geographical resolution is
required to examine within country trends and the effectiveness of the
different public health policies. In this tutorial, we propose a framework
using R to estimate and visualise excess mortality at high geographical
resolution. We show a case study estimating excess deaths during 2020 in Italy.
The proposed framework is fast to implement and allows combining different
models and presenting the results in any age, sex, spatial and temporal
aggregation desired. This makes it particularly powerful and appealing for
online monitoring of the pandemic burden and timely policy making.
