Short-term forecasts of expected deaths

Abstract

We introduce a method for making short-term mortality forecasts of a few months, illustrating it by estimating how many deaths might have happened if some major shock had not occurred. We apply the method to assess excess mortality from March to June 2020 in Denmark and Sweden as a result of the first wave of the coronavirus pandemic; associated policy interventions; and behavioral, healthcare, social, and economic changes. We chose to compare Denmark and Sweden because reliable data were available and because the two countries are similar but chose different responses to COVID-19: Denmark imposed a rather severe lockdown; Sweden did not. We make forecasts by age and sex to predict expected deaths if COVID-19 had not struck. Subtracting these forecasts from observed deaths gives the excess death count. Excess deaths were lower in Denmark than Sweden during the first wave of the pandemic. The later/earlier ratio we propose for shortcasting is easy to understand, requires less data than more elaborate approaches, and may be useful in many countries in making both predictions about the future and the past to study the impact on mortality of coronavirus and other epidemics. In the application to Denmark and Sweden, prediction intervals are narrower and bias is less than when forecasts are based on averages of the last 5 y, as is often done. More generally, later/earlier ratios may prove useful in short-term forecasting of illnesses and births as well as economic and other activity that varies seasonally or periodically.

Excess deaths associated with covid-19 pandemic in 2020: age and sex disaggregated time series analysis in 29 high income countries

“…As highlighted previously, the variability around the estimated excess death in children <15 years is larger than in other age groups owing to sparse data 24. To make the analysis comparable, we presented our findings by five broader age groups.…”

“…These methods provide a point estimate and an upper bound for expected deaths and produce a conservative estimate of excess deaths by considering counts above these thresholds 18. More recently, Rizzi and Vaupel proposed a method to “shortcast” (neologism for short term forecasting) the average number of deaths in the absence of an event that relies exclusively on death counts 24. Their approach is appealing owing to its simplicity but was applied to only four countries, and, as the authors specifically noted, this method is designed for short term forecasting.…”

“…20 Comparing the deaths in the timeline of interest with the simple average of the previous few years ignores critical factors such as recent general health improvement and decline in mortality with time, seasonal and natural variability, and demographic changes. 23 24 More nuanced methods of outbreak detection modelling have been proposed to detect aberrations in mortality on the basis of historical trends. 72 73 74 These methods provide a point estimate and an upper bound for expected deaths and produce a conservative estimate of excess deaths by considering counts above these thresholds.…”

“…18 More recently, Rizzi and Vaupel proposed a method to “shortcast” (neologism for short term forecasting) the average number of deaths in the absence of an event that relies exclusively on death counts. 24 Their approach is appealing owing to its simplicity but was applied to only four countries, and, as the authors specifically noted, this method is designed for short term forecasting. Therefore, this method may not be suitable for the covid-19 pandemic, which continues to exist more than a year after its emergence.…”

“…Some have compared deaths during the pandemic with those reported in 2019 or with a simple average of the preceding few years 20. However, estimates of expected deaths should ideally account for recent temporal trends in mortality that are likely to have occurred in the absence of the pandemic 212324…”