Version 1
: Received: 20 September 2024 / Approved: 20 September 2024 / Online: 20 September 2024 (15:07:32 CEST)
How to cite:
Nalmpatian, A.; Heumann, C.; Pilz, S. Forecasting Mortality Trends: Advanced Techniques and the Impact of COVID-19. Preprints2024, 2024091647. https://doi.org/10.20944/preprints202409.1647.v1
Nalmpatian, A.; Heumann, C.; Pilz, S. Forecasting Mortality Trends: Advanced Techniques and the Impact of COVID-19. Preprints 2024, 2024091647. https://doi.org/10.20944/preprints202409.1647.v1
Nalmpatian, A.; Heumann, C.; Pilz, S. Forecasting Mortality Trends: Advanced Techniques and the Impact of COVID-19. Preprints2024, 2024091647. https://doi.org/10.20944/preprints202409.1647.v1
APA Style
Nalmpatian, A., Heumann, C., & Pilz, S. (2024). Forecasting Mortality Trends: Advanced Techniques and the Impact of COVID-19. Preprints. https://doi.org/10.20944/preprints202409.1647.v1
Chicago/Turabian Style
Nalmpatian, A., Christian Heumann and Stefan Pilz. 2024 "Forecasting Mortality Trends: Advanced Techniques and the Impact of COVID-19" Preprints. https://doi.org/10.20944/preprints202409.1647.v1
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 COVID-19 pandemic on mortality. Utilising 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 the uncertain future mortality trajectories.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
