Belgian Mortality Monitoring Be-MOMO

These are the observed all-cause mortality in Belgium outputted from the Be-MOMO model over the last five years. The daily observed numbers are the average of the day, the 3 previous and the 3 later days. Because of a delay in the registration of deaths, counts for the most recent weeks are preliminary and numbers are visualized with a 3 week time lag. You are free to use the results produced by this application on the sole condition that the source is mentioned (cf. Footnote at the bottom of this page).

This is the observed all-cause mortality from which the officially reported Belgian COVID-19 related deaths (Source) have been substracted and colored in orange. The resulting blue area represents the remaining deaths from all other causes. When the total number of deaths per day exceeds the upper or lower limits of the prediction interval predicted by the modelling (green dashed lines), there is a significant excess or under-mortality. From March to May 2020, we observed an unusual peak in mortality that is almost entirely attributed to COVID-19 related deaths. The peak of COVID-19 death occurred around 8 April. The other peak that is observed in August has been attributed to a heat wave.

This graph shows the observed all-cause mortality in Belgium and the curves of the environmental risks such as meteorological data (temperature) and air pollution data (ozone, PM10 and PM2.5). It allows to visualize the correlations between mortality and extreme temperatures (above 25°C or below 0°C) and high air-pollutant concentrations (ozone above 100µg/m³ (max 8-hour mean), PM10 above 45µg/m³ and PM2.5 above 15µg/m³; corresponding to WHO thresholds of 2021). The vertical light-orange bands highlight the days with statistically significant excess mortality. All values are smoothed with a 7-day centered moving average. Meteorological data are provided by the Royal Meteorological Institute of Belgium (RMI). Ozone, PM10 and PM2.5 data are provided by The Belgian Interregional Environment Agency (IRCEL-CELINE).

Exp. Mort.
Expected Mortality
Obs. Mort.
Observed Mortality
Minimum temperature
Maximum temperature
Particulate Matter < 10µm
Particulate Matter < 2.5µm
How to play with this graph
  • – Hover lines with your mouse to view numbers
  • – Click & drag with your mouse to zoom on a period
  • – Right-click to reset default zoom
  • – Hover legend items to highlight the lines
  • – Click legend items to hide/show the lines

In Belgium, surveillance of all-cause mortality is carried out on a weekly basis by the Infectious Diseases Epidemiology Unit of Sciensano. The mortality monitoring model is designed to serve as a tool for rapid detection and quantification of unusual mortality which might result from disease epidemics such as influenza or from extreme environmental conditions such as heat waves. A timely assessment of the impact on mortality may be useful to guide or reinforce new or existing public health measures, e.g. vaccinations for influenza and the national heat action plan. Moreover, mortality monitoring can be used to evaluate possible effects of public health measures by comparing periods before and after the implementation of the intervention.


Data are updated on a weekly basis, except population sizes for which the official numbers at the 1st of January are used. Mortality and population data are provided by the National Register. The mortality file contains information on all deaths that were registered by the Belgian municipalities during the week before (starting on Saturday and ending on Friday). The data comprise the date of birth, date of death, gender, nationality, place of residence and place of death. The causes of death are unknown. Because of a considerable variation in the rapidness of death registration (ranging from a few days to many weeks after the actual date of death), figures for recent periods are incomplete. Around 95% of mortality data are available after 3 weeks. Deaths taking place abroad are removed from the analyses since these deaths are assumed to be independent of the concurrent environmental conditions in Belgium. Foreigners who die in Belgium are included.

Statistical Methods

Observed deaths are aggregated by day. To be able to detect and quantify important increases in mortality, observed death counts are compared to 2 types of reference lines, obtained by modelling past 5-year mortality data:

  • Expected deaths are the model predictions and represent normal/average mortality levels. They are used for the calculation of the excess number of deaths (observed – expected).
  • The threshold is the upper limit of the prediction interval around expected mortality, calculated by a 2/3-power transformation to correct for skewness in the Poisson distribution (Farrington et al, 1996). Threshold values represent critical mortality levels and are used to detect unusual or significant mortality outbreaks. The confidence level for the upper threshold was chosen as the optimal compromise between sensitivity and specificity of alert detection. It was set at 99.5% for daily-level data.

The statistical model is a modification of the log-linear Farrington model, originally developed for the detection of infectious diseases outbreaks based on weekly disease counts (Farrington et al, 1996). The model was adapted in order to be applicable to both daily- and weekly-level mortality data. While the original method limits the amount of reference data by using only historical data from similar weeks, a sine and cosine wave component was added to capture the seasonal pattern of mortality. This enables modelling the complete 5-year time series and reduces random variation in the predicted baseline, especially for daily-level data. The Be-MOMO model was adapted on 14 June 2021 following the 2020 excess mortality (EN - FR - NL).

An automated analyses procedure is implemented since 2018 in R, software for statistical computing (previously with Stata version 13). Statistical methods and performance of the Be-MOMO system are described in more detail in Cox et al (2010) and in the last summer mortality report.

Cox B, Wuillaume F, Van Oyen H, Maes S. Monitoring of all-cause mortality in Belgium (Be-MOMO): a new and automated system for the early detection and quantification of the mortality impact of public health events. International Journal of Public Health 2010, 55(4):251-259.