From the epidemiological total population to the realistically addressable market population
Why epidemiological funnels are often structurally flawed
The Role of Epidemiological Models in the Business Case
In the life sciences context, epidemiological analyses serve several functions at once. First, they provide an initial quantitative estimate of the addressable patient population. Second, they form the basis for revenue models, market forecasts, and investment assessments.
We have explained how such market models are fundamentally structured and which structural elements must be taken into account in a separate article on Modeling Market Sizes in the Life Sciences Sector .
Especially in early development phases, when clinical data are still limited, such models often become a central reference point for strategic decisions. Investors, management teams, and development partners orient themselves around the market potential derived from them.
In this way, epidemiological models fulfill a role that goes far beyond pure market analysis. They combine scientific development, commercial perspective, and capital allocation within a shared analytical framework.
For that reason, it is worth taking a closer look at how these models are structured.
The Basic Principle of the Epidemiological Funnel
The basic principle of an epidemiological funnel is comparatively simple. A large starting population is gradually reduced to a smaller target population. Typical stages of such a model include, for example:
Total population of a country or region
Prevalence or incidence of a disease
diagnosed patients
patients eligible for treatment
patients actually treated
potential market share of a new therapy
Mathematically, this logic is often represented by a series of multiplicative factors.² Each step further reduces the original population until a realistic target population emerges.
This structure is intuitive and easy to understand. That is precisely why it is built relatively quickly in many business cases.
However, the real challenges often arise between the individual model stages.
Typical Structural Errors in an Epidemiological Funnel
1. The Wrong Epidemiological Starting Point
A common mistake concerns the choice of the starting variable itself.
Many models automatically begin with disease prevalence. In certain situations, however, incidence would be the more appropriate basis. The difference is significant:
Prevalence describes the total number of patients at a specific point in time.
Incidence describes the number of new cases per year.
Which measure is more appropriate depends heavily on the therapy type. While chronic therapies are often driven by prevalence, one-time or curative treatments are more closely aligned with incidence.³
If this difference is not properly accounted for, the very first step in the model can already lead to systematic distortions.
2. Diagnostic Rates Are Assumed Implicitly
A second structural error relates to the diagnostic rate.
Many epidemiological models implicitly assume that a large share of patients has been diagnosed. In reality, this is often not the case. Especially in rare diseases or complex clinical pictures, a substantial proportion of patients remains undiagnosed for years. Studies show that diagnostic gaps in many indications have a material impact on the actually addressable population.⁴
If this factor is not explicitly taken into account in the model, the potential market size is systematically overstated.
3. Treatment Eligibility Is Modeled Too Broadly
Even within the diagnosed population, not every patient is automatically a suitable candidate for a new therapy.
The actual target population may be limited by various factors:
disease stage
comorbidities
biomarker-based patient selection
regulatory approval indications
Especially with modern therapeutic approaches, such as in precision oncology or gene therapies, this filter can exclude a significant share of the population.
If these constraints are not modeled cleanly in the funnel, market sizes emerge that appear mathematically correct but are not clinically realistic.
4. Dependencies Between Funnel Stages Are Ignored
Another structural problem in many models is that the individual filters are treated as independent of one another.
In reality, however, many of these factors are closely interconnected. For example:
diagnostic rates often depend on disease stage.
screening programs influence both diagnostic rates and treatment eligibility.
new therapy options also change diagnostic behavior over the long term.
If these interdependencies are not taken into account in the model, small errors can compound significantly across multiple model stages.
5. The Funnel Is Adjusted to Fit the Desired Outcome
A particularly problematic pattern arises when epidemiological models are built in reverse.
In such cases, a desired market potential is defined first, for example on the basis of comparable products, and the funnel is then adjusted so that this result is reached. In such cases, the model does fulfill a communicative function, but it has limited analytical value.
Why These Errors Occur So Frequently
Many structural problems in epidemiological models do not arise from a lack of expertise, but from the conditions of early development phases. At this stage, numerous variables are still uncertain:
actual diagnostic rates
future screening programs
regulatory indication definitions
competitive dynamics
Epidemiological models therefore inevitably have to work with assumptions.
What matters, however, is whether these assumptions are structured transparently and reviewed systematically.
Epidemiological Models as a Tool for Strategic Decision-Making
The biggest mistake when working with epidemiological funnels often lies in how they are interpreted. They are often understood as a tool for displaying market size. In reality, they serve a far more important function. A well-constructed epidemiological model makes it possible to
make key assumptions transparent
identify critical uncertainties
identify sensitivities in the business case
Especially in early development phases, this transparency is often more valuable than a seemingly precise market size figure. The epidemiological funnel thus becomes less a static market analysis and more a tool for structuring strategic decisions.
Conclusion
Epidemiological funnels are a central tool in market modeling within the life sciences sector. When built correctly, they make it possible to derive the actually addressable patient population in a structured manner.
In practice, however, many models produce results determined less by robust data than by the structural assumptions built into the model itself. A reliable epidemiological funnel is therefore defined not only by valid data sources, but above all by a logically consistent model structure.
Only when epidemiological data, diagnostic processes, clinical criteria, and healthcare systems are considered together does a realistic picture of the market potential of a new therapy emerge.
About Excellere LifeScience Consulting
Excellere LifeScience Consulting supports life science companies and investors in the structured assessment of market potential, the development of robust business cases, and the strategic preparation of market entries.
