The data is based upon weekly deaths data from eurostat spanning from 2010 to 2022. For some countries (such as Germany) data is unavailable; and for others it is only available for certain selected age groups.
Countries: DE (Germany)
Source for Weekly Deaths (Eurostat): Eurostat table: Deaths by week, sex and 5-year age group.
Source for Vaccination data: European Centre for Disease Prevention and Control.
Source for Population Estimates (Eurostat and UN): Eurostat table: Population on 1st January by age, sex and type of projection.
Comment on the available data and its limitations.
The data on excess deaths and vaccination has several limitations that one should be aware of when analysing the charts below. Unlike the data from Eurostat for the majority of European countries), the data for weekly deaths in the Germany is only available in older age groups, namely (40-49, 50-59, 60-69, 70-79, 80+ and Total). Any analysis of the data does not allow us to observe the excess mortality in younger age groups with sufficient detail.
Additionally, the European CDPC data on vaccination for Germany is limited to the whole population. This poses a problem as we are unable to show excess deaths versus vaccination rates for different age groups.
In the charts below, we can also show the excess deaths age groups compared to total vaccinations administered, in the following way:
|Excess Deaths Age Group||Vaccination Age Group|
We show the analysis of excess mortality using the different methodologies described in our methodology papers. These papers illustrate the pitfalls and advantages of using the different calculation methods for excess mortality.
In summary, using method 1, excess deaths for 2020, 2021, and 2022 are computed by subtracting the N-year pre-pandemic average deaths (typically 3 to 5 years) from actual deaths in the Covid-19 pandemic years. This method is the one most widely used for estimating excess deaths, including by countries' statistical offices. However, this method has obvious pitfalls as the measurement of excess deaths is highly sensitive to the baseline for estimating "normal" deaths. By using a prior average of the number of deaths as a baseline, biases are easily introduced due to increasing or decreasing population over time. When the population is increasing, excess deaths are over-estimated while when populations are decreasing they tend to be under-estimated.
Method 2 solves some of these problems by computing changes in death rates relative to a given baseline and, as shown in our methodology papers, are much more reliable for estimating excess mortality.
The chart below shows the actual deaths (or death rate) versus the projected estimates when using the different methodologies described in our methodology papers.
The user can select the calculation method and age group. 2022 deaths or deaths rates are annualised estimates using a linear model.
The chart below shows the excess mortality for 2020, 2021 and 2022 (either excess deaths or excess death rates) for a given age group. Please be aware that we matched the age groups as described in the data section above.
The plot allows the user to select the desired methodology and age group. 2022 deaths or deaths rates are annualised estimates using a linear model.
The following chart shows the analysis of excess mortality for 2020, 2021 and 2022, for different age groups. The user can specify the method for estimating excess mortality.
Please be aware that for the US, the vaccination age groups do not match perfectly the excess deaths age groups as mentioned before. We matched the age groups as described in the data section above.