This document discusses how epidemiology and forecasting in the pharmaceutical industry are changing. Traditionally, forecasts were based on prevalence data and static patient models. However, the industry is shifting towards prevention and earlier treatment, requiring forecasts based on incidence and dynamic patient models that account for factors like disease progression, treatment switching, and market uptake rates. Dynamic models provide more accurate forecasts by considering how patient populations and treatment paradigms evolve over time. They also help demonstrate cost-benefits to regulators and payers, as the industry faces increasing pressure for cost containment. Going forward, epidemiology and forecasting will need to move from static to dynamic approaches to keep up with these changes.

1. Epidemiology & Forecasting
The changing face of
epidemiology & forecasting

2. Outline
• Traditional uses of epidemiology /
patient data in pharma forecasting
• Emerging markets
• Changing face of the pharma industry
• Future for epidemiology in forecasting

3. Pharma models – the traditional role
of patients in market forecasting
• Because of the chronic nature of the majority of
pharmaceutical products, any patient driven forecast tends to
be based on prevalence.
– Straight forward measure informing total number of patients alive with
a disease at a point in time
– Patients are diagnosed, then allocated to drug therapy to produce
patient shares (from here the step to revenue is evident)
– Patient-based forecasts also come into their own in markets where
there is either no market data, or the data that exist are extremely
difficult to match up to patients (e.g. markets with insufficient level of
homogeneity).
• Including patients as the foundation to a market model, as
well as any data on existing treatments, can provide a more
informed forecast

4. Prevalence-based Forecasting
Total Prevalent Population
Diagnosed/Treated

5. Forecasting Emerging Markets
• Markets with little or no data.
• Can be applied to new territories or new tech /
disease areas.
• Regardless, there is a wealth of population, health
& disease data available for any country (WHO,
CDC etc).
• Even for seemingly new diseases, data can be
found 99% of the time (with a little digging, applied
intelligence & understanding ).

6. The changing face of Healthcare,
Disease & the World Economy
• Chronic disease burden higher than ever.
• Healthcare costs forecast to increase drastically
over next decade (predominantly western
societies).
• Increased pressure on cost-containment.
• Overall movement towards prevention & “early
health”, coupled with remote patient
monitoring/self-monitoring.
• And all this was moving into place long before we
hit the global recession…..

7. Impact for the Pharma Industry
• Overall movement towards prevention & “early
health”.
– Companies acquiring /collaborating or expanding into
vaccines & diagnostics
– Development of therapeutics targeted earlier in the
disease pathway
• Maximisation of current assets
– Deeper understanding of market dynamics to drive more
accurate forecasts & strategy
• Increased focus on health economics & market
access
– Pressure from regulators & payers to demonstrate cost-
benefit

8. What this means for the
Forecast……
And the Analyst 

9. “Early Health” - Vaccines &
Diagnostics
• No longer forecasting ‘disease’ population per se
• Requires an understanding of risk, incidence or
both
• More in-depth disease knowledge will be required
– Diagnostics, more individuals screened than those in the
‘prevalent’ population
• Greater investigative research skills required by
analyst
• More support & insight for senior management
when viewing the revenue forecasts

10. Revenue Profile of a New Vaccine
/ Diagnostic
Assumes a ‘one-shot’ dose/diagnostic. Acute vs chronic use

11. Increased Market Knowledge -
Market dynamics
• Understanding & forecasting using patient
segments
– New/naïve, switch, restarts etc
• Links product uptake & utilisation to:
– Rate of churn within the market
– Patient status (esp. important for HIV & oncology) –
naïve/drug resistant, previous line of therapy etc
– Marketing strategy
• Gain more accurate/defendable forecasts if known
where patients can be accessed & at what rate
• More support & insight for senior management
when reviewing forecasts & assumptions (esp.
uptake rates)

12. Market Dynamic Product Share

13. Forecast Accuracy & Market
Dynamics
• One contributory cause for inaccurate forecasts, especially
for new products, is an uptake rate into the market that is
unrealistic
– Often driven by finance or marketing
– Need to achieve “X%” market share within first year of launch
• Market dynamics may not support patient share capture if
there are not enough:
– New patients for which new product can be used
– Patients on existing therapies that can be switched to new product
• ‘Dynamic’ patients within the market will dictate rate of
uptake
• Understanding these dynamics (and what can happen during
the launch phase) can not only provide a more accurate
forecast but can illuminate areas of focus for marketing

14. Dynamic Patient Modelling in
Practice
• Of course, if it was that easy we’d all be doing it
already
• By moving towards a more dynamic view of patients (be
it from an incidence/prevalence perspective or new &
switch patients), the level of model complexity & data
required to drive it is increased.
• It also increases the ‘black-box’ nature of any model as
although the maths can still stay relatively straight-
forward, the understanding of the mechanics of the
model becomes more abstract
• Markets where it is important to understand this:
– HIV (chronic markets where drug resistance is an issue),
– Oncology (where line of therapy is an issue, high mortality)

15. Static vs. Dynamic
Patient Based Models
Used for markets where treatment paradigms
and patient populations are not changing over
time
– Static, cross-sectional
– Isolated variables
– Capture equilibrium
– Concrete
– Transparent
– Spreadsheet software
– Data-Driven
Patient Flow Models
Used for markets where treatment paradigms
and patients are in transition or evolving
– Dynamic, transitional
– Interactive, relational variables
– Capture continuous state changes
– Abstract, conceptual
– Can sometimes be ‘black-box’
– Complex system software
By severity
%diagnosed
%treated
By severity
%diagnosed
%treated
treateduntreated
OI recurrence
withdraw
fail
rebound death
death maintain
treateduntreated
OI recurrence
withdraw
fail
rebound death
death maintain
Reproduced with kind permission from G. Heaney

16. Step-change in Perspective
• Dynamic forecast modelling is quite
different to the traditional ‘static’
approach where many variables were
‘implicit’
• Relationships need to be well understood
(such as incidence, prevalence &
mortality, start of therapy, duration,
point of switch)
• Can find some unexpected results when
testing the logical limits of the model..

17. Future for Epidemiology in
Forecasting

18. Move towards Dynamic Forecasts
• Early health
– Move from prevalence to incidence/
recurrence & risk evaluation
• Market dynamics
– From static snapshot to patient flow
• Market access & health economics
– Dynamic patient modelling

19. Market Access & Dynamic Patient
Modelling
• With increasingly limited health care resources,
regulators & payers are demanding more information
regarding cost-benefit / savings to budgets etc before
awarding price & reimbursement
• Dynamic disease models are on the rise as they allow
companies to demonstrate effectiveness of their
compound throughout a disease pathway to show:
– Anticipated reductions in downstream events
– Improved outcomes & health status/utility of a given
population
– Expected savings to health care budgets & resources
• Models of this type are invaluable not only as tools to
investigate price & reimbursement, but throughout the
drug pipeline to find thresholds for drug candidates (in
terms of efficacy) & assess potential acquisitions.

20. Questions?