Month-to-month all-cause mortality forecasting: A method allowing for changes in seasonal patterns

Ainhoa-Elena Leger*, Silvia Rizzi

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


Forecasting of seasonal mortality patterns can provide useful information for planning healthcare demand and capacity. Timely mortality forecasts are needed during severe winter spikes and/or pandemic waves to guide policy-making and public health decisions. In this study, we propose a flexible method to forecast all-cause mortality in real-time considering short-term changes in seasonal patterns within an epidemiological year. All-cause mortality data has the advantage of being available with less delay than cause-specific mortality data. We use all-cause monthly death counts from national statistical offices for Denmark, France, Spain, and Sweden from seasons 2012/13 through 2021/22 to demonstrate the performance of the proposed approach. The method forecasts the deaths one-month-ahead, based on their expected ratio to the next month. Prediction intervals are obtained via bootstrapping. The forecasts accurately predict the winter peaks before COVID-19. Although the method predicts mortality less accurately during the first wave of the COVID-19 pandemic, it captures the aspects of later waves better than other traditional methods. The method is attractive for health researchers and governmental offices to aid public health responses because it uses minimal input data, makes simple and intuitive assumptions, and provides accurate forecasts both during seasonal influenza epidemics and during novel virus pandemics.
Original languageEnglish
JournalAmerican Journal of Epidemiology
Publication statusPublished - 2024


  • Short-term mortality forecasting
  • all-cause mortality
  • seasonality
  • public health surveillance data
  • mortality shocks


Dive into the research topics of 'Month-to-month all-cause mortality forecasting: A method allowing for changes in seasonal patterns'. Together they form a unique fingerprint.

Cite this