  The current outbreak of COVID-19 has called renewed attention to the need for
sound statistical analysis for monitoring mortality patterns and trends over
time. Excess mortality has been suggested as the most appropriate indicator to
measure the overall burden of the pandemic on mortality. As such, excess
mortality has received considerable interest during the first months of the
COVID-19 pandemic. Previous approaches to estimate excess mortality are
somewhat limited, as they do not include sufficiently long-term trends,
correlations among different demographic and geographic groups, and the
autocorrelations in the mortality time series. This might lead to biased
estimates of excess mortality, as random mortality fluctuations may be
misinterpreted as excess mortality. We present a blend of classical
epidemiological approaches to estimating excess mortality during extraordinary
events with an established demographic approach in mortality forecasting,
namely a Lee-Carter type model, which covers the named limitations and draws a
more realistic picture of the excess mortality. We illustrate our approach
using weekly age- and sex-specific mortality data for 19 countries and the
current COVID-19 pandemic as a case study. Our proposed model provides a
general framework that can be applied to future pandemics as well as to monitor
excess mortality from specific causes of deaths.
