The rising cost of Long COVID and ME/CFS in Germany
For the first time, a new report released by Risklayer and the ME/CFS Research Foundation models the prevalence and cost of Long COVID and ME/CFS in Germany. The report looked at the entire five-year period between 2020 and 2024 and found that both diseases created a substantial societal burden. This website summarises key findings and conclusions from the report. The full report, which includes a detailed description of the model developed to generate the novel data, can be accessed via the download link below.
Presentation of results at the ME/CFS Symposium 2025
What are the key findings?
In addition to causing acute illness and global disruption, the COVID-19 pandemic has triggered a growing wave of long-term health conditions that continue to affect millions of people, resulting in rising socio-economic costs. Aside from what is known as Long COVID or post-COVID syndrome, ME/CFS (myalgic encephalomyelitis / chronic fatigue syndrome), too, has long been suspected to cause substantial costs to society. Until now, neither the total number of people affected by both conditions, nor the extent to which the two diseases are causing damage in terms of economic, social, and medical costs to society in Germany has been known.
With this new report, released jointly by the ME/CFS Research Foundation and Risklayer, a first-of-its-kind holistic attempt at modelling the number of people living with either ME/CFS or Long COVID and the costs arising from these burdensome diseases in Germany is made publicly available. The findings of the report paint a stark picture: For the five-year period between 2020 and 2024, Long COVID and ME/CFS cost Germany more than €250 billion. In 2024 alone, Long COVID and ME/CFS cost €63.1 billion, equating to 1.5% of the country's gross domestic product (GDP) the same year. Annual costs over the entire period modelled are displayed in Figure 6 and Table 1 of the report.
Using an innovative approach, by combing existing data and findings from literature with novel data from a model specifically developed for this report, the authors show that, at the end of 2024, 871,086 people in Germany were likely living with Long COVID, while an additional 650,183 people were living with ME/CFS (the latter includes ME/CFS diagnosed as a result of COVID-19/Long COVID). See also Table 1 of the report. In total, more than 1.5 million people in Germany were living with either Long COVID or ME/CFS at the end of 2024.
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How does the model work?
The underlying data basis for modelling the prevalence of Long COVID and ME/CFS for the purpose of this report stems from a corrected modelling of the number of monthly SARS-CoV-2 infections in Germany since the start of the COVID-19 pandemic. This alternative model is available as a peer-review publication. It indicates that in 2023-2024, the number of SARS-CoV-2 infections was likely 80-100 times higher than official data by the Robert Koch Institute (the federal public health institute in Germany) suggests. This can be seen in Figure 1 of the report.
Based on the corrected number of SARS-CoV-2 infections, the report's own data model, which was specifically developed by the authors, generates data on the number of active Long COVID as well as ME/CFS cases in Germany and how these developed over time. While Long COVID cases peaked in 2022, before reducing to a more stable level as SARS-CoV-2 became endemic in the population, according to the model, the number of ME/CFS cases steadily increased during the pandemic (Figure 3). Indeed, a number of international experts predicted that the already comparably high number of people living with ME/CFS in the general population would likely substantially increase with the spread of SARS-CoV-2.
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Click on the image to open a detailed view and download the image in high quality.
In order to generate data on the number of active Long COVID and ME/CFS cases over time, the report's data model makes use of both the corrected number of SARS-CoV-2 infections (see Figure 1) as well as a range of existing findings from scientific literature. These findings cover assumptions on how many of the people who contract SARS-CoV-2 go on to develop Long COVID, how many of these Long COVID cases remain ill for more than a year and how many of the Long COVID cases transition to ME/CFS. The model also takes into account the share of people recovering from Long COVID and ME/CFS over time (for ME/CFS the overall recovery rate has been found to be very low, only at around 5% per year). Key assumptions and how these are fit into the model' flow are depicted in Figure 8 of the report.
On the basis of the modelled number of Long COVID and ME/CFS cases, the model proceeds with defining the costs arising from the two diseases. These costs result from reduced value-add and increased expenditure, calculated according to standard economic parameters. Specifically, the model calculates the following costs: production disturbance costs, human capital costs, medical costs, administrative costs, travel costs, support and assistance costs, deadweight costs of transfer payments, and quality of life and well-being costs. These are then multiplied by different severity multipliers for Long COVID and ME/CFS, where a disability or severity weighting is a factor on a scale from 0 to 100% which reflects the severity of health loss associated with the particular condition, where 0 represents full health and 100% represents a full burden and not being able to work (see Annex 2 in the report for a detailed description). The model generates monthly costs arising from Long COVID and ME/CFS over the observed period, as illustrated in Figure 5 of the report. The sum off all these costs results in the total combined cost of more than €250 billion for 2020-2024.
As can be seen in Figure 5 of the report, the monthly costs of Long COVID and ME/CFS remained at a more or less stable and high level over the course of the past 20 or so months. Currently, there is no reason to assume that the high level of costs is going to abate on its own.
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Download the full report!
Press reports on the study (selection, all in German language):
- Der Spiegel “Long Covid und ME/CFS kosten die Gesellschaft jährlich 60 Milliarden Euro” (external link)
- FAZ – Frankfurter Allgemeine: “Folgekosten der Pandemie: Der lange Arm des Virus” (external link)
- Manager Magazin: “Long Covid und ME/CFS: Kosten in Milliardenhöhe in Deutschland” (external link)
- Der Tagesspiegel: “Studie zu Folgen von Virusinfektion: Long Covid und ME/CFS kosten Gesellschaft 60 …” (external link)
- Der Tagesspiegel: “Die 60-Milliarden-Krankheit: Warum es richtig ist, die Kosten von Long Covid akribisch vorzurechnen” (external link)
- Deutsches Ärzteblatt: “Medienbericht: Long COVID und ME/CFS kosten Gesellschaft 63 Milliarden Euro jährlich” (external link)
- Frankfurter Rundschau: “Erschreckende Zahlen: Long Covid kostet Deutschland etliche Milliarden pro Jahr” (external link)
- Frankfurter Allgemeine Presse: “Alarmierende Statistik: Deutschland verliert jährlich Milliarden durch Long Covid” (external link)
- N-TV Nachrichten: “Hunderttausende sind erkrankt: Long Covid und ME/CFS kosten 60 Milliarden Euro pro Jahr” (external link)
- NZZ – Neue Züricher Zeitung: “Long Covid und ME/CFS: Milliardenkosten belasten Deutschland” (external link)
- AOK: “250 Milliarden Folgekosten durch Long Covid und ME/CFS seit 2020” (external link)
Download the full report!
Why is this data model important?
In the absence of any reliable data on the prevalence and the costs of Long COVID and ME/CFS in Germany, it has remained difficult for stakeholders, such as the German government and, for example, the country’s pharmaceutical sector, to measure up an adequate and justified response in terms of public spending and investment in research. So far, some €150 million have been made available for health care services research by the Federal Ministry of Health (BMG) , with another roughly €50 million provided for clinical and basic research by the Federal Ministry of Education and Research (BMBF). As such, the federal government spent approximately €40 million annually on Long COVID and ME/CFS research during the five-year period up until the end of 2024. Compared to the combined cost of both diseases in Germany in 2024 of €63.1 billion, average annual spending on research by the federal government equaled 0.06%. Some state governments, too, have made funds available for dedicated research projects. However, a coordinated, nationwide research strategy has yet to be implemented.
Some efforts by the federal government of Germany are underway to address the current evidence gap on prevalence and costs. Such efforts include the Federal Joint Committee (G-BA)-funded project BD-LC-PS, the MultiCARE and HELoCO projects funded by the BMG, and the Post-COVID Data Model by the Federal Ministry of the Interior (BMI).
With any official data unlikely to be available soon, the ME/CFS Research Foundation and Risklayer felt it was important to provide interim – and in the eyes of the authors currently the most up-to-date and complete – estimates on the prevalence and cost of Long COVID and ME/CFS in Germany. With the release of their report, both organisations intend to inform wide-ranging discussions about how best to shape public policy, funding, research, and health systems to improve overall outcomes and reduce costs for people living with Long COVID and ME/CFS, communities, governments and society at large.
What are the conclusions?
German state and federal governments have been proactive in raising awareness and improving the overall care situation for people living with Long COVID and ME/CFS, as well as providing funding for basic, clinical and epidemiological research. That said, general efforts and levels of funding allocated to advance and improve the research, care and treatment of ME/CFS historically, and both ME/CFS and Long COVID to the present day, remain disproportionately low compared with the modelled costs and even when compared with similarly burdensome diseases.
1. Improving our baseline understanding of Long COVID and ME/CFS
There is currently a lack of surveillance, inconsistent diagnostic practices underpinned by multiple systems of classification, and patchy knowledge amongst medical practitioners, employers and the public. All of which act as barriers to improving baseline data about the two diseases. These factors also contribute to poor health, social, and economic outcomes in a variety of ways, including via stigmatisation and chronic stress. Improved surveillance will help, and more can be done in terms of improving education and knowledge about these conditions in a wide range of settings.
2. Lowering the overall infection rate (e.g. through public health measures and vaccinations)
Public health interventions, strategies, technologies and policies have the potential to reduce future costs in terms of lowering the overall infection rate, alongside vaccinations. In this context, improving air quality in public buildings by enforcing existing regulations in Germany as well as in health, education, school and childcare settings, should be considered.
3. Reducing severity and increasing recovery rates (e.g. by improving treatment and/or developing a cure) are important areas to target
Reducing severity and increasing recovery rates of Long COVID and ME/CFS may be achieved through a combination of targeted biomedical research and improved health systems infrastructure. Resourcing basic, clinical, and translational research to identify underlying disease mechanisms, uncover biomarkers, build diagnostic capabilities and develop effective treatments, including novel drugs, could significantly reduce the costs of these conditions and improve quality of life for the hundreds of thousands of patients in Germany (and millions of people globally).
Noteworthy, while the model only looked at historical data, it can be safely assumed that the number of active ME/CFS cases alone will likely rise by a total of another around 34,000 in the period of 2025-2028, based on the SARS-CoV-2 infections and Long COVID cases that have already taken place at this point. It can therefore be concluded that the issue of Long COVID and ME/CFS will not naturally attenuate, with COVID-19 now being endemic and in the absence of any currently available cures or effective treatments.
In comparison to the combined cost incurred over the past five years (around €250 billion), the amount of money spent by the federal government on research to counter the negative effects of these two conditions over the same period (around €200 million) clearly pales in comparison. The findings of the report can therefore be used by a variety of stakeholders to strongly argue in favour of a substantial expansion of public health interventions and research funding, by both the public and private sector alike.
What are the limitations of the report?
Limitations of the report and its data model relate first and foremost to the general lack of data. This includes a lack of infection surveillance (such as cases of COVID-19 overall, Long COVID and ME/CFS incidence and prevalence, fluctuation of non-COVID-19-related ME/CFS cases during the various stages of the pandemic and related public health measures, data on recovery rates for both Long COVID and ME/CFS), as well as lack of long-term data due to the relative newness of Long COVID.
This means there is insufficient longitudinal data to understand completely the effects of vaccination, infection-derived immunity, different virus strains, geographical location, or other factors on disease progression and resolution. Where necessary, assumptions were made in the model about these based on the best available secondary data. Limitations also relate to biased data. Where the model draws on secondary data, such as employer and health insurer data, a number of biases might be present.
Noteworthy, among other things, the model assumes that non-COVID-19-related ME/CFS case numbers remained constant over time, with only COVID-19-related ME/CFS having increased since 2020. This means the modelled number of ME/CFS cases is likely an underestimation.
Another limitation of the model used in the report is its use of the so-called Monte Carlo simulation, which is computationally intensive and require significant expertise to carry out. This type of simulation cannot easily be re-run, explained for non-specialists to understand, or replicated by all reviewers for complete verification. Annex 2 of the report aims to provide sufficient details on this modelling approach. The model data is also provided on GitHub, .
The report provides an overview of other, existing studies which have taken similar approaches at estimating and modelling the different costs of Long COVID and ME/CFS (Tables 2 and 3 in Annex 1 of the report). That the model’s various findings broadly align with research published by others lends further weight to its reliability. Generally, the report’s authors concluded that the model’s findings are conservative and more likely to underestimate rather than overestimate costs.
Who are the authors?
The data-based modelling in this report was performed by risk management experts from Risklayer GmbH. Risklayer has been providing risk analytics and risk management to governments all over the world since 2014. For the initial years of the COVID-19 pandemic, Risklayer maintained a detailed day-by-day SARS-CoV-2 infections caseload dataset for all 401 districts in Germany, based in part on a novel crowdsourcing approach for data. The comprehensive data provided by Risklayer was utilized and frequently cited by major German media outlets and as the basis for official counting of cases and trends in Germany through the first two years of the pandemic.
Through this practical experience, Risklayer has cultivated extensive expertise in COVID-19 caseload data within Germany. This proficiency is augmented by Risklayer's ongoing practice of modelling the consequences of critical events and natural disasters, from seismic activity and severe meteorological occurrences to conflict. The reliability of Risklayer's models is evidenced by their daily use by numerous governments and non-governmental organisations globally. Risklayer originated from the Center for Disaster Management and Risk Reduction Technology (CEDIM) at the Karlsruhe Institute of Technology (KIT) in Germany, and the General Sir John Monash Foundation in Australia.
The ME/CFS Research Foundation provided input on the assumptions used in the model, as well as literature reviews and engagement with networks of experts. As a non-profit organisation based in Germany, and with an international scientific advisory board, the Foundation funds biomedical research, enables the networking of researchers and experts in the field, and informs patients and the public about the status quo of research relating to both ME/CFS and subtypes of Long COVID.
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