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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?
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
A young independent EU agency dedicated to the prevention and
control of communicable diseases
The role of ECDC?
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
Identify, assess and communicate current and emerging health threats
to human health from communicable diseases.
— ECDC Founding Regulation (851/2004), Article 3
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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?
Couldn’t you
model it?
We really – don’t know
Oh dear
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A worrying statement
modelling generates hypothesesidentifies, quantifies uncertainty, tells you what to look for test hypotheses
Modelling has shown
that ….
Modelling suggests
that
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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 AsaikainenJohn Beddington Simon CauchemezNeil FergusonPeter GroveDidier HoussinMaria van Kerkhove Marianne van der SandeHelen Shirley-QuirkJacco WallingaPeter WhiteBut the views and opinions are mine …..
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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
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??
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The point is ….
Like there are many types of doctorsThere 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
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
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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
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
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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
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
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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 counter-measures including pharmaceuticals
• Immunogenicity – how well immunity occurs
• The safety of pharmaceutical interventions
And then there are the Unknown Unknowns
Many successful examples of modelling
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Real-time outbreak analysis
• UK Foot and Mouth Disease epidemic (2001) – modelling guided control
policy.
0
50
100
150
200
250
300
350
400
450
18-Feb 4-Mar 18-Mar 1-Apr 15-Apr 29-Apr 13-May 27-May 10-Jun 24-Jun 8-Jul
Date
Co
nfi
rme
d d
aily
ca
se
inc
ide
nc
e
A: Several Days to Slaughter
B: Slaughter on infected premiseswithin 24 hours
C: Slaughter on infected andneighbouring farms within 24 and 48hours, respectively
Data up to 29 March
Data from 30 March
A
B
C
Model predictions by Dr Neil Ferguson, Dr Christl Donnelly & Prof. Roy Anderson, Imperial College
0
20
40
60
80
100
120
22-F
eb
1-M
ar
8-M
ar
15-M
ar
22-M
ar
29-M
ar
5-A
pr
12-A
pr
19-A
pr
26-A
pr
3-M
ay
10-M
ay
17-M
ay
24-M
ay
31-M
ay
• SARS 2003 – estimates of transmissibility (R0~3) and CFR
(~15%). 0
100
200
300
400
500
1980 1983 1986 1989 1992 1995
Year
Nu
mb
er (
thou
sand
s)
New Infections
Cases
• BSE/vCJD (1995) – estimates of exposure, modelling of risk-
reduction.
Models explain complex dynamics, can generate and sometimes even test hypotheses but always need validation 36
Some Errors - Grove’s Rules
1. To believe the Modelling
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
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Communication Communication Communication
One version of the truth
Force the modellers to agreeDon’t introduce them at different levels
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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 estimate420 deaths upper estimate840 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 modellingOr a ‘translator’
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But so what?
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 WHODeployment 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
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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
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_
Assessment_Acid_Tests.pdf
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Conclusions - 1 Good Things – Not so Good ThingsWhat modelling is good at with influenza (may
work): Planning – what might happenPost-event analyses – what did happenWhat needs to be determined – e.g. rapid
seroepidemiologyWhat 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 RulesManaging expectations is key Link to actionEducate the Policy Makers Try to get away from numbersCommunicationsLink to Actions
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Acknowledgements Again
Tommi AsaikainenJohn Beddington Simon CauchemezNeil FergusonPeter GroveDidier HoussinMaria van Kerkhove Marianne van der SandeHelen Shirley-QuirkJacco WallingaPeter WhiteBut the views and opinions are mine …..
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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