Forecasting Mortality Trends: Advanced Techniques and the Impact of COVID-19

Asmik Nalmpatian (), Christian Heumann and Stefan Pilz
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Asmik Nalmpatian: Department of Statistics, Ludwig Maximilian University of Munich, 80539 Munich, Germany
Christian Heumann: Department of Statistics, Ludwig Maximilian University of Munich, 80539 Munich, Germany
Stefan Pilz: Department of Statistics, Ludwig Maximilian University of Munich, 80539 Munich, Germany

Stats, 2024, vol. 7, issue 4, 1-17

Abstract: The objective of this research is to evaluate four distinct models for multi-population mortality projection in order to ascertain the most effective approach for forecasting the impact of the COVID-19 pandemic on mortality. Utilizing data from the Human Mortality Database for five countries—Finland, Germany, Italy, the Netherlands, and the United States—the study identifies the generalized additive model (GAM) within the age–period–cohort (APC) analytical framework as the most promising for precise mortality forecasts. Consequently, this model serves as the basis for projecting the impact of the COVID-19 pandemic on future mortality rates. By examining various pandemic scenarios, ranging from mild to severe, the study concludes that projections assuming a diminishing impact of the pandemic over time are most consistent, especially for middle-aged and elderly populations. Projections derived from the superior GAM-APC model offer guidance for strategic planning and decision-making within sectors facing the challenges posed by extreme historical mortality events and uncertain future mortality trajectories.

Keywords: mortality modeling; COVID impact; multi-populational; cross-country; generalized additive models; partial APC plots; APC; machine learning; excess mortality (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2024
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