Forecasting Mortality Trends: Advanced Techniques and the Impact of COVID-19
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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-10-01
|
| Series: | Stats |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-905X/7/4/69 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850037145226444800 |
|---|---|
| author | Asmik Nalmpatian Christian Heumann Stefan Pilz |
| author_facet | Asmik Nalmpatian Christian Heumann Stefan Pilz |
| author_sort | Asmik Nalmpatian |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-82e736cd8bbb4df782ecd0c162e20aad |
| institution | DOAJ |
| issn | 2571-905X |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Stats |
| spelling | doaj-art-82e736cd8bbb4df782ecd0c162e20aad2025-08-20T02:56:58ZengMDPI AGStats2571-905X2024-10-01741172118810.3390/stats7040069Forecasting Mortality Trends: Advanced Techniques and the Impact of COVID-19Asmik Nalmpatian0Christian Heumann1Stefan Pilz2Department of Statistics, Ludwig Maximilian University of Munich, 80539 Munich, GermanyDepartment of Statistics, Ludwig Maximilian University of Munich, 80539 Munich, GermanyDepartment of Statistics, Ludwig Maximilian University of Munich, 80539 Munich, GermanyThe 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.https://www.mdpi.com/2571-905X/7/4/69mortality modelingCOVID impactmulti-populationalcross-countrygeneralized additive modelspartial APC plots |
| spellingShingle | Asmik Nalmpatian Christian Heumann Stefan Pilz Forecasting Mortality Trends: Advanced Techniques and the Impact of COVID-19 Stats mortality modeling COVID impact multi-populational cross-country generalized additive models partial APC plots |
| title | Forecasting Mortality Trends: Advanced Techniques and the Impact of COVID-19 |
| title_full | Forecasting Mortality Trends: Advanced Techniques and the Impact of COVID-19 |
| title_fullStr | Forecasting Mortality Trends: Advanced Techniques and the Impact of COVID-19 |
| title_full_unstemmed | Forecasting Mortality Trends: Advanced Techniques and the Impact of COVID-19 |
| title_short | Forecasting Mortality Trends: Advanced Techniques and the Impact of COVID-19 |
| title_sort | forecasting mortality trends advanced techniques and the impact of covid 19 |
| topic | mortality modeling COVID impact multi-populational cross-country generalized additive models partial APC plots |
| url | https://www.mdpi.com/2571-905X/7/4/69 |
| work_keys_str_mv | AT asmiknalmpatian forecastingmortalitytrendsadvancedtechniquesandtheimpactofcovid19 AT christianheumann forecastingmortalitytrendsadvancedtechniquesandtheimpactofcovid19 AT stefanpilz forecastingmortalitytrendsadvancedtechniquesandtheimpactofcovid19 |