Modelling and short-term forecasting of seasonal mortality
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917924393975808 |
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| author | Leger, Ainhoa-Elena Rizzi, Silvia Basellini, Ugofilippo |
| author_facet | Leger, Ainhoa-Elena Rizzi, Silvia Basellini, Ugofilippo |
| contents | Excess mortality, i.e. the difference between expected and observed mortality, is used to quantify the death toll of mortality shocks, such as infectious disease-related epidemics and pandemics. However, predictions of expected mortality are sensitive to model assumptions. Among three specifications of a Serfling-Poisson regression for seasonal mortality, we analyse which one yields the most accurate predictions. We compare the Serfling-Poisson models with: 1) parametric effect for the trend and seasonality (SP), 2) non-parametric effect for the trend and seasonality (SP-STSS), also known as modulation model, and 3) non-parametric effect for the trend and parametric effect for the seasonality (SP-STFS). Forecasting is achieved with P-splines smoothing. The SP-STFS model resulted in more accurate historical forecasts of monthly rates from national statistical offices in 25 European countries. An application to the COVID-19 pandemic years illustrates how excess mortality can be used to evaluate the vulnerability of populations and aid public health planning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10787 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Modelling and short-term forecasting of seasonal mortality Leger, Ainhoa-Elena Rizzi, Silvia Basellini, Ugofilippo Applications Excess mortality, i.e. the difference between expected and observed mortality, is used to quantify the death toll of mortality shocks, such as infectious disease-related epidemics and pandemics. However, predictions of expected mortality are sensitive to model assumptions. Among three specifications of a Serfling-Poisson regression for seasonal mortality, we analyse which one yields the most accurate predictions. We compare the Serfling-Poisson models with: 1) parametric effect for the trend and seasonality (SP), 2) non-parametric effect for the trend and seasonality (SP-STSS), also known as modulation model, and 3) non-parametric effect for the trend and parametric effect for the seasonality (SP-STFS). Forecasting is achieved with P-splines smoothing. The SP-STFS model resulted in more accurate historical forecasts of monthly rates from national statistical offices in 25 European countries. An application to the COVID-19 pandemic years illustrates how excess mortality can be used to evaluate the vulnerability of populations and aid public health planning. |
| title | Modelling and short-term forecasting of seasonal mortality |
| topic | Applications |
| url | https://arxiv.org/abs/2502.10787 |