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.
Environmental Risk Factors
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
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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.
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.
- NL – Oversterfte tijdens de eerste en tweede golf van de COVID-19-epidemie in België
- FR – Surmortalité durant la 1re et 2e vague de l'épidémie de COVID-19 en Belgique
- EN – Excess mortality during the first and second waves of the COVID-19 epidemic in Belgium
- FR – Hiver 2018-2019 – Belgique
Surveillance de la mortalité toutes causes confondues en Belgique, Flandre, Wallonie et Bruxelles durant l'hiver 2018-2019
- NL – Winter 2018-2019 – België
Surveillance van sterfte door alle oorzaken in België, Vlaanderen, Wallonië en Brussel in de winter van 2018-2019
- FR – Été 2018 – Belgique
Surveillance de la mortalité toutes causes en Belgique, Flandre, Wallonie et Bruxelles durant l'été 2018
- NL – Zomer 2018 – België
Surveillance van de mortaliteit door alle oorzaken in België, Vlaanderen, Wallonië en Brussel tijdens de zomer van 2018
- FR – Hiver 2017—2018
Surveillance de la mortalité en Belgique, Flandre, Wallonie et Bruxelles durant l'hiver 2017—2018
- NL – Winter 2017—2018
Surveillance van de mortaliteit in België, Vlaanderen, Wallonië en Brussel in de winter van 2017—2018
- FR – Été 2017 – Belgique
Surveillance de la mortalité toutes causes en Belgique durant l'été 2017
- NL – Zomer 2017 – Vlaanderen
Surveillance van de mortaliteit door alle oorzaken in Vlaanderen tijdens de zomer van 2017
- NL – Zomer 2017 - België
Surveillance van de mortaliteit door alle oorzaken in België tijdens de zomer van 2017
- Weekly influenza bulletin
- Influenza end of season reports
- All-cause mortality supports the COVID-19 mortality in Belgium and comparison with major fatal events of the last century
Natalia Bustos Sierra, Nathalie Bossuyt, Toon Braeye, Mathias Leroy, Isabelle Moyersoen, Ilse Peeters, Aline Scohy, Johan Van der Heyden, Herman Van Oyen & Françoise Renard
Archives of Public Health volume 78, Article number: 117 (2020).
- Excess all-cause mortality during the COVID-19 pandemic in Europe – preliminary pooled estimates from the EuroMOMO network, March to April 2020
Lasse S Vestergaard, Jens Nielsen, Lukas Richter, Daniela Schmid, Natalia Bustos, Toon Braeye et al.
Eurosurveillance Vol 25, N°26 (June 2020).
- European all-cause excess and influenza-attributable mortality in the 2017/18 season : should the burden of influenza B be reconsidered?
Nielsen J, Vestergaard L, Richter L, Schmid D, Bustos Sierra N, Asikainen T et al.
Clinical Microbiology and Infection. 10.1016/j.cmi.2019.02.011. (February 2019)
- Excess all-cause and influenza-attributable mortality in Europe, December 2016 to February 2017.
Vestergaard L, Nielsen J, Krause T, Espenhain L, Tersago K, Bustos Sierra N et al.
Euro Surveillance Vol. 22, N°14 (April 2017).
- Excess mortality among the elderly in European countries, December 2014 to February 2015.
Mølbak K, Espenhain L, Nielsen J, Tersago K, Bossuyt N et al.
Euro Surveillance Vol. 20, N°11 (March 2015).
- Excess mortality among the elderly in 12 European countries, February and March 2012.
Mazick A, Gergonne B, Nielsen J, Wuillaume F et al.
Euro Surveillance Vol. 17, N° 14 (April 2012).
- Higher all-cause mortality in children during autumn 2009 compared with the three previous years : pooled results from eight European countries.
Mazick A, Gergonne B, Wuillaume F, et al.
Euro Surveillance Vol.15, N°5 (February 2010).
- 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.
Bianca Cox, Francoise Wuillaume, Herman Van Oyen, Sophie Maes.
International journal of public health. 55. 251-9. 10.1007/s00038-010-0135-6 (April 2010).
- Death toll exceeded 70,000 in Europe during the summer of 2003.
Robine JM, Cheung SL, Le Roy S, Van Oyen H et al.
Comptes Rendus Biologies, Vol. 331, N°2 (2008), 171-178.