Month-to-month all-cause mortality forecasting: A method to rapidly detect changes in seasonal patterns

Forecasting of seasonal mortality patterns can provide useful information for planning healthcare demand and capacity. Timely mortality forecasts are especially needed during severe winter spikes and/or pandemic waves to guide policy-making and public health decisions. We propose a flexible method to forecast all-cause mortality in real-time and to rapidly identify short-term changes in seasonal patterns within an epidemiological year. All-cause mortality data has the advantage of being available in many countries 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 previous month. Prediction intervals are obtained via bootstrapping. The forecasts, for the analyzed countries, predict the winter and summer mortality peaks over the epidemiological years before COVID-19 and reflect the synchrony and mortality burden of COVID-19 waves. 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.

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