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The current outbreak of COVID-19 has called renewed attention to the need forsound statistical analysis for monitoring mortality patterns and trends overtime. Excess mortality has been suggested as the most appropriate indicator tomeasure the overall burden of the pandemic on mortality. As such, excessmortality has received considerable interest during the first months of theCOVID-19 pandemic. Previous approaches to estimate excess mortality aresomewhat limited, as they do not include sufficiently long-term trends,correlations among different demographic and geographic groups, and theautocorrelations in the mortality time series. This might lead to biasedestimates of excess mortality, as random mortality fluctuations may bemisinterpreted as excess mortality. We present a blend of classicalepidemiological approaches to estimating excess mortality during extraordinaryevents with an established demographic approach in mortality forecasting,namely a Lee-Carter type model, which covers the named limitations and draws amore realistic picture of the excess mortality. We illustrate our approachusing weekly age- and sex-specific mortality data for 19 countries and thecurrent COVID-19 pandemic as a case study. Our proposed model provides ageneral framework that can be applied to future pandemics as well as to monitorexcess mortality from specific causes of deaths.

Assessing Excess Mortality in Times of Pandemics Based on Principal Component Analysis of Weekly Mortality Data -- The Case of COVID-19