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Professor Angus Nicoll CBE
European Centre for Disease Prevention and Control
“Mathematical Modeling –
Help or Hindrance?”
Plenary Session 3 – Options for the Control of Influenza VII – September 6th 2010
What is ECDC?
A young independent EU agency dedicated to
the prevention and
control of communicable diseases
 Emerging and re-emerging communicable
diseases revitalised through globalisation,
bio-terrorism, interconnectivity, and an EU
without internal borders
 Health implications of enlarging EU
 Strengthen EU public health capacity to help
meet EU citizen's concerns
The role of ECDC?
Identify, assess and communicate current
and emerging health threats to human
health from communicable diseases.
— ECDC Founding Regulation (851/2004), Article 3
 EU level disease surveillance and
epidemic intelligence
 Scientific opinions and studies
 Early Warning System and
response
 Technical assistance and training
 Communication to scientific
community
 Communication to the public
Declaration of Interests
• No relevant commercial interests
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Declaration of Interests
• No relevant commercial interests
• Salary from government sources
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Declaration of Interests
• No relevant commercial interests
• Salary from government sources
• Not a modeller
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Declaration of Interests
•
•
•
•
No relevant commercial interests
Salary from government sources
Not a modeller
Some of my best friends are modellers
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Declaration of Interests
•
•
•
•
•
No relevant commercial interests
Salary from government sources
Not a modeller
Some of my best friends are modellers
Some of my colleagues seem to have strong
views about modelling ! * !
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The three ages of a development
Enthusiasm –
“Lets model it …..” The wonderful
solution (to all uncertainty)
Disillusionment – “But you said there would be …”
Hopeless - Confusing –
Less used the better
Realism -
Very useful in some circumstances, but
must be used with care and reservations
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A worrying conversation
So what’s
going to
happen?
Oh
dear
We really –
don’t know
Couldn’t
you model
it?
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A worrying statement
Modelling
has shown
that ….
Modelling
suggests
that
modelling generates hypotheses
identifies, quantifies uncertainty, tells you
what to look for test hypotheses
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So how was this talk prepared?
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So how was this talk prepared?
I asked modellers and policy
developers / makers
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Acknowledgements
Tommi Asaikainen
John Beddington
Simon Cauchemez
Neil Ferguson
Peter Grove
Didier Houssin
Maria van Kerkhove
Marianne van der Sande
Helen Shirley-Quirk
Jacco Wallinga
Peter White
But the views and opinions are mine …..
14
Plan of Talk
•
•
•
•
•
•
•
•
An unusual talk about modelling
Definitions
Types of modellers and modelling
Why pandemic flu is so difficult
Grove’s rules
Communication Issues
Link to Surveillance and Action
Conclusions
15
Definition of modelling: 1. simple
….a construction of known
conceptual simplifications of any
system under consideration which
can then be analysed
mathematically…..
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Definition – 2. more complex
….. a simplified mathematical representation of a
complex process, device, or concept by means of a
number of variables which are defined to represent the
inputs, outputs, and internal states of the device or
process, and by which something one understands, a
theory, can be applied to …..
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“for every complex, difficult
problem there is frequently a
solution that is simple,
attractive…”
“for every complex, difficult
problem there is frequently a
solution that is simple,
attractive…”
– and liable to be wrong
Adapted from HL Mencken (humorist)
Not all models are
mathematical
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Modellers - a collective noun?
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Modellers - a collective noun?
a crowd of people,
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Modellers - a collective noun?
a crowd of people, a flock of birds,
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Modellers - a collective noun?
a crowd of people, a flock of birds, a
mischief of mice,
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Modellers - a collective noun?
a crowd of people, a flock of birds, a
mischief of mice, a busyness of
ferrets,
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Modellers - a collective noun?
a crowd of people, a flock of birds, a
mischief of mice, a busyness of
ferrets, a farrow of pigs,
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Modellers - a collective noun?
a crowd of people, a flock of birds, a
mischief of mice, a busyness of
ferrets, a farrow of pigs, a
distribution of modellers??
27
The point is ….
Like there are many types of doctors
There are many types of modellers and modelling even just
within public health and infectious diseases
Some specialise in:
• Particular diseases
• Networks analysis
• Health Economics
• Operational modelling
…. And much more
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Why is flu, and especially
pandemic flu so difficult
The complexity of transmission patterns
Multiple interacting factors affect transmission patterns – so complex
Understanding infectious disease epidemiology requires modelling to
synthesise evidence from multiple sources
• Contact patterns, % infections symptomatic, % seeking care, vaccine efficacy,
vaccine uptake.
→ Multidisciplinary: needs clinical, behavioural, biological, statistical,
mathematical knowledge
Modelling links individual-level processes to population-level effects, e.g.
• vaccination directly protects individuals – and has a population level
effect (herd immunity)
• decline in child-child contacts over the summer reduced infection
incidence
For any pandemic virus – what can and
cannot be assumed?
• What probably can be assumed:
Known knowns
• Modes of transmission (droplet, direct
and indirect contact)
• Broad incubation period and serial
interval
• At what stage a person is infectious
• Broad clinical presentation and case
definition (what influenza looks like)
• The general effectiveness of personal
hygiene measures (frequent hand
washing, using tissues properly,
staying at home when you get ill)
• That in temperate zones transmission
will be lower in the spring and summer
than in the autumn and winter
What cannot be assumed:
The known unknowns
• Antigenic type and
phenotype
• Susceptibility/resistance
to anti-virals
• Age and clinical groups
most affected
• Age-groups with most
transmission
• Clinical attack rates
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For any pandemic virus – what can and
cannot be assumed?
• What probably can be assumed:
Known knowns
• Modes of transmission (droplet, direct
and indirect contact)
• Broad incubation period and serial
interval
• At what stage a person is infectious
• Broad clinical presentation and case
definition (what influenza looks like)
• The general effectiveness of personal
hygiene measures (frequent hand
washing, using tissues properly,
staying at home when you get ill)
• That in temperate zones transmission
will be lower in the spring and
summer than in the autumn and
winter
What cannot be assumed:
The known unknowns
• Pathogenicity (case-fatality
rates)
• ‘Severity’ of the pandemic
• Precise parameters needed
for modelling and
forecasting (serial interval,
transmissibility = R)
• Precise clinical case
definition & sub-clinical
infections
• The duration, shape,
number and tempo of the
waves of infection
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For any pandemic virus – what can and
cannot be assumed?
• What probably can be assumed:
Known knowns
• Modes of transmission (droplet,
direct and indirect contact)
• Broad incubation period and serial
interval
• At what stage a person is infectious
• Broad clinical presentation and case
definition (what influenza looks like)
• The general effectiveness of
personal hygiene measures (frequent
hand washing, using tissues
properly, staying at home when you
get ill)
• That in temperate zones
transmission will be lower in the
spring and summer than in the
autumn and winter
What cannot be assumed:
The known unknowns
• Will new virus dominate over
seasonal type A influenza?
• What are the complicating
conditions (super-infections
etc.)
• The effectiveness of
interventions and countermeasures including
pharmaceuticals
• Immunogenicity – how well
immunity occurs
• The safety of pharmaceutical
interventions
And then there are the Unknown
Unknowns
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Many successful examples of
modelling
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Real-time outbreak analysis
• BSE/vCJD (1995) – estimates of
exposure, modelling of risk-reduction.
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Number (thousands)
New Infections
• UK Foot and Mouth Disease epidemic
(2001) – modelling guided control policy.
• SARS 2003 – estimates of
transmissibility (R0~3) and CFR (~15%).
400
Cases
300
200
100
0
1980
A: Several Days to Slaughter
A
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C: Slaughter on infected and
neighbouring farms within 24 and 48
hours, respectively
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Data up to 29 March
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150
20
C
Date
31-May
24-May
17-May
10-May
3-May
26-Apr
19-Apr
12-Apr
5-Apr
8-Jul
29-Mar
18-Feb 4-Mar 18-Mar 1-Apr 15-Apr 29-Apr 13-May 27-May 10-Jun 24-Jun
22-Mar
0
0
15-Mar
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B
Data from 30 March
8-Mar
100
1995
Year
1-Mar
Confirmed daily case incidence
200
1992
100
B: Slaughter on infected premises
within 24 hours
300
250
1989
120
400
350
1986
Model predictions by Dr Neil Ferguson, Dr Christl Donnelly & Prof. Roy Anderson, Imperial College
22-Feb
450
1983
Models explain complex
dynamics, can generate and
sometimes even test hypotheses
but always need validation
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Some Errors - Grove’s Rules
1.
To believe the Modelling
It’s not magic……
Two Errors or Grove’s Rules
1.
To believe the Modelling
Two Errors – Grove’s Rules
1.
To believe the Modelling
2.
Not to listen to the Modellers
A third Error – Grove’s Rules
1.
To believe the Modelling
2.
Not to listen to the Modellers
3.
Not to seek validation – surveillance
data
Communication Communication
Communication
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One version of the truth
Force the modellers to agree
Don’t introduce them at
different levels
A danger – when the message from
modelling is ‘passaged’ - Stille Post
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An example – where it can go wrong how many
people are going to die from the pandemic in one
country?
What was estimated and said range of - 3,100 to 65,000
http://www.bbc.co.uk/blogs/thereporters/ferguswalsh/2009/07/
Britain prepares for 65,000 deaths from swine flu
http://www.timesonline.co.uk/tol/life_and_style/health/article6716477.ece
Don't panic over swine flu death pleads health boss ...
17 Jul 2009 ... they predict 65,000 deaths from swine flu in a year
www.thisiswiltshire.co.uk/.../4498484.
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How the ‘predictions’ evolved
July 17th 2009 range of - 3,100 to 65,000 deaths
By Sept 2009 For Winter – Autumn wave – Diagnosed deaths
70 deaths lower estimate
420 deaths upper estimate
840 deaths reasonable worse case
By February 2010 – 242
Conclusion - don’t give out estimates when there is a
lot of uncertainty
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Modest but tough modellers who can say ‘No’
and understand policy concerns
Educated politicians with some understanding
of limits of modelling
Or a ‘translator’
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But so what?
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Surveillance – Surveillance - Surveillance
Surveillance – Surveillance - Surveillance
Should be information for action
Surveillance in a Pandemic
The Parameters and Rationales
Strategic Parameter
Rationale for knowing
(what actions follow)
Identify and monitor changing
phenotypic / genotypic characteristics
of the pandemic strain in Europe.
Provide timely and representative
virological input data to WHO
Deployment of human avian influenza
vaccine (if A/H5 type).
Determine antiviral resistance pattern
to direct initial recommendations on
use of antivirals
Broad estimate of severity of the
pandemic – ECDC Severity Matrix
Determining the limits of public health
actions that are justified
Surveillance in a Pandemic
The Parameters and Rationales
Strategic Parameter
Rationale for knowing
(what actions follow)
Confirm / determine case definition and
its predictive value
Confirm or refine default case definition
for offering testing / treatment
(antivirals)
To determine when laboratories can
reduce the amount of confirmatory
testing of cases
Give relative estimates of incidence and
disease by age-group or other risk
parameters (e.g. those with chronic
conditions, pregnant women)
Target interventions and refine
countermeasures e.g. who to give
antivirals and human avian influenza and
specific pandemic vaccines
So then we have to/had to adapt generic plans to fit
the reality of any specific pandemic – operational
modelling for options
'No battle plan ever survives
contact with the enemy…'
― Field Marshall Helmuth Carl
Bernard von Moltke,
1800–1891
I.e. we had generic pandemic
plans, but then we had to adapt
them to the specific features
peculiar to this pandemic.
Statue of Helmuth von Moltke the
Elder, Berlin
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ECDC’s Acid Local Tests
1.Can local services robustly and effectively deliver
anti-virals to most of those that need them inside
the time limit of 48 hours since start of
symptoms?
5. Can local hospitals increase ventilatory support (
intensive care) for influenza patients including
attending to issues including staff training,
equipment and supplies?
ECDC Acid Tests
http://www.ecdc.europa.eu/en/healthtopics/Documents/0702_Local_Assessm
ent_Acid_Tests.pdf
Conclusions - 1
Good Things – Not so Good Things
What modelling is good at with influenza (may work):
Planning – what might happen
Post-event analyses – what did happen
What needs to be determined – e.g. rapid seroepidemiology
What might work
What certainly will not work
What is more challenging (probably won’t work):
Use in the midst of the pandemic
‘Now-casting’
Forecasting – predicting
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Conclusions -2
Groves Rules
Managing expectations is key
Link to action
Educate the Policy Makers
Try to get away from numbers
Communications
Link to Actions
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Acknowledgements Again
Tommi Asaikainen
John Beddington
Simon Cauchemez
Neil Ferguson
Peter Grove
Didier Houssin
Maria van Kerkhove
Marianne van der Sande
Helen Shirley-Quirk
Jacco Wallinga
Peter White
But the views and opinions are mine …..
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The unexpected developments for 2009 :
The unknown unknowns
• The severe cases – with the severe cases being primary viral
pneumonitis causing Acute Respiratory Distress Syndrome .
• That intensive-care units would be under so much pressure.
• That the pandemic would be so mild for most people.
• That because of the mild threat for most people there would be
criticism of ‘over-preparation’ or ‘over-investment’ in vaccines.
• That the pandemic vaccines would show such a good
immunological response to a single injection in adults – but will
this be sustained over time?
• That there would be resistance and doubt among the
professionals in some countries on the value of the
countermeasures
• That some people would question this was a pandemic at all
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