Professor Angus Nicoll discussed using models for infectious disease policymaking. He explained that models are simplifications that can help quantify uncertainty and identify knowledge gaps. Early in an outbreak, many factors are unknown, including severity, clinical characteristics, and transmission parameters needed for modeling. Communication between modelers and policymakers is important, as models inform but do not determine policy. Surveillance data are also critical for validating and improving model estimates over time.

1. Professor Angus Nicoll CBE
European Centre for Disease Prevention and Control
Using and Developing Models for Epidemic
Infectious Disease Policy
– Some examples from Influenza
Modelling for Policy, The British Academy Conference May 17-18 2012, UK

2. ECDC? What and Why
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

3. 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
 Risk Assessment
 Early Warning System and response
 Technical assistance and training
 Communication with the scientific community
 Communication to the public

4. 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 ! * !
• Actual DoI here
4

5. The three ages of a modelling development
Enthusiasm – “Lets model it …..”
The solution (to all uncertainty)
Disillusion – “But you said there would be ……”
“Hopeless “ – “Confusing – “
“Can’t you agree….!!”
“Give me a one-handed modeller!
Realism - Very useful in some circumstances,
Not a substitute for having data,
Communication is paramount,
Policy makers used to working with modellers &
vice-versa,
Groves Rules
5

6. Variable Relationships between Science,
Social Science and Policy
Science determining policies
vs:
Science informing policies
vs:
Science justifying pre-determined or cultural
policies
vs:
Science-free policies (which may include
independent scientific activities in the
countries)
And what do we mean by Science?

7. A worrying conversation
So what’s
going to
happen?
We really –
Oh don’t know
dear
Couldn’t
you model
it?
7

8. A worrying statement
Modelling
has shown
that ….
Modelling
suggests
that
modelling generates hypotheses
identifies, quantifies uncertainty, tells you
what to look for, to modify & test hypotheses
8

9. So how was this talk prepared?
I have worked constantly with …
and asked modellers and policy
developers / makers
It has developed over time 9

10. Acknowledgements
Tommi Asaikainen
John Beddington
Simon Cauchemez
Marco Cavaleri
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 …..
10

11. Plan of Talk
• An unusual talk about modelling
• Some limitations
• Some definitions
• Types of modellers and modelling
• Why infections can be so difficult
• Grove’s rules
• Communication Issues
• The ECDC approach - link to Surveillance and Action
• Conclusions
• Further reading
11

12. Limitations of this talk
The uses of modelling in dealing with infections are legion! In
a short talk not dealing with the following in any detail
• Determining and comparing burdens due to particular
infections and then the most useful countermeasures
• Vaccine preventable disease – determining the likely impact
of specific vaccines – including health economics
• Determining how infections are likely to spread through
studying of contact patterns
• Estimating parameters from scanty data in a crisis
• Virological Risk Assessment
• Spread of pathogens in the environment
12

13. Modelling
Always a simplification
13

14. Definition of modelling: 1. simple
….a construction of known
conceptual simplifications of any
system under consideration which
can then be analysed
mathematically….. 14

15. 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 …..
15

16. Modelling
Always a simplification
– so beware!
16

17. “for every complex, difficult
problem there is frequently a
solution that is simple,
attractive…”

18. “for every complex, difficult
problem there is frequently a
solution that is simple,
attractive…”
– and liable to be wrong
Adapted from HL Mencken (humorist)

19. Not all models are
mathematical
19

20. 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
Modellers tend to specialise
Some specialise in:
• Particular diseases or groups of diseases
• Policy work
• Networks analysis
• Health Economics
• Operational modelling – rarely
…. and much more
20

21. Epidemic infections, influenza,
and especially pandemic flu so
difficult…. Why?

22. The Need for Speed
An example - SARS

23. The Metropole Hotel Hong Kong

25. Hotel M Floor
Diagram
938
Lift for Flat
Disabled Roof
Persons
Service
room
902 904 906 908 910
Lift Lift Hall 924
Hall
911 915 917 925
index 2 cases
Previously known Source case Study case
cases

26. If the significance of the event could have
been realised at the time, or at least earlier!
Fortunately SARS was not so
epidemic as influenza
26

27. Complexity of transmission patterns
Multiple interacting factors affect transmission patterns – so complex
Understanding infectious disease epidemiology requires modelling and
analysis to synthesise evidence from multiple sources
• Contact patterns, % infections symptomatic, % seeking care, vaccine effectiveness,
vaccine uptake, knowledge of the disease .
→ Multidisciplinary: needs linked virological
information, clinical, behavioural, biological,
statistical, mathematical knowledge Example –
Mexico 2009
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

28. The ECDC Approach to flu, (and
other epidemic respiratory diseases)
Apologies to an American Sage

29. For any pandemic virus – what can and
cannot be assumed?
What cannot be assumed:
• What probably can be assumed:
The known unknowns
Known knowns
• Modes of transmission (droplet, direct • Antigenic type and
and indirect contact)
• Broad incubation period and serial
phenotype
interval
• At what stage a person is infectious
• Susceptibility/resistance
• Broad clinical presentation and case to anti-virals
definition (what influenza looks like)
• The general effectiveness of personal • Age and clinical groups
hygiene measures (frequent hand
washing, using tissues properly, most affected
staying at home when you get ill)
• That in temperate zones transmission • Age-groups with most
will be lower in the spring and summer
than in the autumn and winter
transmission
• Clinical attack rates
29

30. For any pandemic virus – what can and
cannot be assumed?
What cannot be assumed:
• What probably can be assumed:
The known unknowns
Known knowns
• Modes of transmission (droplet, direct • Pathogenicity (case-fatality
and indirect contact) rates)
• Broad incubation period and serial
interval • ‘Severity’ of the pandemic
• At what stage a person is infectious
• Broad clinical presentation and case
• Precise parameters needed
definition (what influenza looks like) for modelling and
• The general effectiveness of personal
hygiene measures (frequent hand
forecasting (serial interval,
washing, using tissues properly, transmissibility = R)
staying at home when you get ill)
• That in temperate zones transmission
• Precise clinical case
will be lower in the spring and definition & sub-clinical
summer than in the autumn and
winter infections
• The duration, shape,
number and tempo of the
waves of infection
30

31. For any pandemic virus – what can and
cannot be assumed?
What cannot be assumed:
• What probably can be assumed:
The known unknowns
Known knowns
• Modes of transmission (droplet, • Will new virus dominate over
direct and indirect contact) seasonal type A influenza?
• Broad incubation period and serial
interval
• What are the complicating
• At what stage a person is infectious
conditions (super-infections
• Broad clinical presentation and case
etc.)
definition (what influenza looks like) • The effectiveness of
• The general effectiveness of interventions and counter-
personal hygiene measures (frequent
hand washing, using tissues
measures including
properly, staying at home when you pharmaceuticals
get ill)
• Immunogenicity – how well
• That in temperate zones
transmission will be lower in the immunity occurs
spring and summer than in the • The safety of pharmaceutical
autumn and winter
interventions
And then there are the Unknown
Unknowns
31

32. 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
32

33. Not all of these are equally important
The most important are those
that can determine policy
33

34. Many successful examples of
modelling
….. but determining policy?
34

35. Variable Relationships between Science,
Social Science and Policy
Science determining policies
vs:
Science informing policies
vs:
Science justifying pre-determined or cultural
policies
vs:
Science-free policies (which may include
independent scientific activities in the
countries)
And what do we mean by Science?

36. Real-time outbreak analysis
• BSE/vCJD (1995) – estimates of
500
exposure, modelling of risk-reduction. 400
New Infections
Number (thousands)
Cases
• UK Foot and Mouth Disease epidemic 300
(2001) – modelling guided control policy. 200
• SARS 2003 – estimates of 100
transmissibility (R0~3) and CFR (~15%).
0
1980 1983 1986 1989 1992 1995
Model predictions by Dr Neil Ferguson, Dr Christl Donnelly & Prof. Roy Anderson, Imperial College
Year
450
A: Several Days to Slaughter
400 120
Confirmed daily case incidence
350 B: Slaughter on infected premises 100
within 24 hours
300
A 80
C: Slaughter on infected and
250 neighbouring farms within 24 and 48
hours, respectively 60
200
Data up to 29 March
150 40
Data from 30 March B
100 20
50 C 0
1-Mar
8-Mar
5-Apr
15-Mar
22-Mar
29-Mar
12-Apr
19-Apr
26-Apr
22-Feb
3-May
10-May
17-May
24-May
31-May
0
18-Feb 4-Mar 18-Mar 1-Apr 15-Apr 29-Apr 13-May 27-May 10-Jun 24-Jun 8-Jul
Date

37. Models explain complex
dynamics, can generate and
sometimes even test hypotheses
but always need validation
37

38. Some Errors - Grove’s Rules
1. To believe the Modelling

39. It’s not magic……

40. Grove’s Rules
1. To believe the Modelling (that this will happen)
2. Not to listen to the Modellers
3. Not to seek validation – surveillance data

41. Communication Communication
Communication
41

42. “One version of the truth”
Force the modellers to agree

43. “One version of the truth”
Don’t introduce modellers at
different levels = chaos and loss
of confidence

44. A danger – when the message from
modelling is ‘passaged’ - Stille Post
44

45. Early Mortality Estimates Tend
to Be Higher than later
45

46. 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
deaths 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.
46

47. How the ‘predictions’ evolved – a European
Example from the UK Modelling Group
Estimates were made from early on but in private – early wide
confidence limits - then a statement was made
July 17th 2009 range of - 3,100 to 65,000 deaths
By Sept 2009 For Winter – Autumn wave – Diagnosed and
reported deaths: 70 deaths lower estimate
420 deaths upper estimate
840 deaths reasonable worse case
By February 2010 – 242 deaths
Conclusion - try not to give estimates when there is a
lot of uncertainty – especially the upper ones
47

48. Early influenza mortality
estimates tend to be higher than
later
…but not always 48

49. Modest but tough modellers who can say ‘No’
we do not know (yet) and understand policy
concerns
Educated politicians with some understanding
of limits of modelling
Or a ‘translator’ 49

50. What Helps – a lot
50

51. Surveillance – Surveillance - Surveillance
These data allow there to be
validation and improvments of
estimates

52. The source of these data and analyses that
come from them
• Confirmed cases
• Hospital and mortality based data
• Virologic surveillance information
• Syndromic surveillance with virology
• Telephone and web-based surveys
• Outbreak investigations with serology
• Clinical cases series
• Serologic data
• Always best to combine virology, clinical &
epidemiological information
52

53. The ECDC Approach Again
Operational Severity
53

54. Surveillance – Surveillance - Surveillance
Should focus on information for
action

55. What are the Decisions?
A decision to respond
The scale of the response
Mitigation and infection control
Measures to protect individuals -
prioritisation
Measures to reduce and slow
transmission
Investment allocation
Timing of the responses
55

56. Surveillance in a Pandemic
The Parameters and Rationales
Strategic Parameter Rationale for knowing
(what actions follow)
Identify and monitor changing Provide timely and representative
phenotypic / genotypic characteristics virological input data to WHO
of the pandemic strain in Europe. 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 Determining the limits of public health
pandemic – ECDC Severity Matrix actions that are justified

57. Surveillance in a Pandemic
The Parameters and Rationales
Strategic Parameter Rationale for knowing
(what actions follow)
Confirm / determine case definition and Confirm or refine default case definition
its predictive value for offering testing / treatment
(antivirals)
To determine when laboratories can
reduce the amount of confirmatory
testing of cases
Give relative estimates of incidence and Target interventions and refine
disease by age-group or other risk countermeasures e.g. who to give
parameters (e.g. those with chronic antivirals and human avian influenza and
conditions, pregnant women) specific pandemic vaccines

58. Operational Aspects
58

59. 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
59

60. 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

61. Conclusions – Modelling, Policies & Acute
Infections Crises
Good Things – Not so Good Things
What modelling is good at with infections (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
It helps if the disease is ‘slower moving’
What is more challenging (probably won’t work):
Use in the midst of the pandemic
‘Forecasting – predicting
Now-casting’ - a spcial case
61

62. 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
62

63. Selected Further Reading
Van Kerkhove MD, et al 2010 Studies Needed to Address Public Health Challenges of the 2009
H1N1 Influenza Pandemic: Insights from Modelling. PLoS Med 7(6): e1000275.
doi:10.1371/journal.pmed.1000275
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2879409/
Van Kerkhove MD , Ferguson NM. Epidemic and intervention modelling – a
scientific rationale for policy decisions? Lessons from the 2009 influenza
pandemic’ Bull WHO 2012 http://www.who.int/bulletin/volumes/90/4/11-
097949/en/index.html
Nicoll A, et al Experience and lessons from surveillance and studies of the 2009 pandemic in
Europe. Public Health 2010 124:14–23. Available here
Timpka T, Eriksson H, Gursky EA, Nyce JN, Morin M, Jenvald J et al Population-based
simulations of influenza pandemics: validity and significance for public health policy. Bull
WHO 2009; 87: 305-311.
Kenah E, Chao DL, Matrajt L, Hallioran ME, Longinin IM. The global transmission and control
of influenza. Plos One 2011; 6 (5) e19515
Truscott J et al Essential epidemiological mechanisms underpinning the transmission dynamics
of seasonal influenza. J R Soc Med Interface 2011
Lipsitch M, et al Improving the evidence base for decision making during a
pandemic: the example of 2009 influenza A(H1N1) Biosecurity and Bioterrorism,
Biodefence Strategy, Practice and Science 2011; 9: 89-115.
63

64. Spare Slides
64

65. 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
65

66. Some difficult firsts of the 2009 pandemic 1
The first pandemic to emerge in the twenty-first
century. It has been more widespread and remains
ongoing …. Compare with SARS.
The first pandemic to occur after major global
investments in pandemic preparedness had been
initiated.
The first pandemic where effective vaccines and
antivirals were widely available in many countries,
thus requiring public health authorities to
earn and retain the confidence of health care
providers through whom such are usually
distributed.

67. Some difficult ‘firsts’ - 2
The first pandemic that took place within the context
of a set of International Health Regulations and
global governance, which had not been tested until
the present.
The first pandemic with early diagnostic tests that led
to rapid diagnosis - but also an early obsession in
the media and of policymakers with the numbers of
those infected.
The first pandemic with antivirals available in many
countries that led to a hopeful expectation that the
pandemic might be containable, leading to the
implementation of a “containment phase”.

68. Some difficult ‘firsts’ - 3
The first pandemic where intensive care was
available in many countries to treat critically ill
patients, - fostering an expectation that everyone
could be treated and cured.
The first pandemic with instant communication so
that early impressions (such as the experience and
initial government overreaction in underprepared
counties: Argentina, Mexico, Ukraine) could be
shared ahead of any scientific analysis.
The first pandemic with a “blogosphere” and other
rapid communication tools that were perilous to
ignore – but difficult to counter.
From: Leung G, Nicoll A. Initial reflections on pandemic A(H1N1) 2009 and the international response.
Plos Medicine October 2010
http://www.plosmedicine.org/article/info%3Adoi%2F10.1371%2Fjournal.pmed.1000346

69. 2009 pandemic could have been a lot
worse for Europe! (Situation April 2010)
• A pandemic strain emerging in the Americas. A pandemic emerging in a
developing country
• Immediate virus sharing so rapid diagnostic and
vaccines. Delayed virus
sharing
• Based on A(H1N1) currently not that
Based on a more
pathogenic and without pathogenicity markers. pathogenic strain, e.g.
A(H5N1)
• Residual immunity in much of a large group
(older people). No residual
immunity
• Sustained susceptibility to oseltamivir.
Inbuilt antiviral
• Good data and information coming out of resistance
North America and the southern hemisphere.
Minimal data until
• Arriving in Europe in the summer. transmission reached
Europe
• A relatively prepared region Arriving in the late
Little preparation y autumn or winter
• Mild presentation in most infected.
Contrast with what
• A highly immunogenic virus & vaccine might have
Severe presentation
immediately happened
A vaccine needing more than one injection
and considerable antigen
69

70. 2009 Pandemic Myths and Reality
Topic ? Believed Reality
WHO Statements Talked up the pandemic Certainly did not
Pandemic Was not a pandemic at all Fitted all definitions
Pandemic Definition WHO changed it to fit the Certainly did not (but WHO
pandemic – removing could have been more
severity – Flynn report explicit in definition)
Mortality Hardly anyone died – only Reported deaths are a
2900 deaths in Europe fraction of attributable
additional deaths
Comparison with Was just like seasonal Similarities but at least 9
seasonal influenza influenza or milder significant differences
Vaccination experience Nobody wanted to be Depends on the country
vaccinated
Pharmaceutical Drug companies had vast Unclear, what BMJ
company vaccine profits—$7bn to $10bn reported was arithmetically
profits from the from vaccines alone - BMJ wrong speculation of
pandemic Editorial from Flynn Report potential sales

71. 2009 Pandemic Myths and Reality
Nicoll A, McKee M. Moderate pandemic, not many dead. Learning the right lessons in Europe from the 2009 pandemic EJPH October
2010 vol. 20 no. 5 486-488 doi:10.1093/eurpub/ckq114 http://eurpub.oxfordjournals.org/content/20/5/486.full and
http://eurpub.oxfordjournals.org/content/suppl/2010/09/27/ckq114.DC1/ckq114_suppl.pdf
Topic ? Believed Reality
WHO Talked up the Certainly did not
Statements pandemic
Pandemic Was not a pandemic Fitted all
at all definitions
Pandemic WHO changed it to Certainly did not
Definition fit the pandemic – (but should have
removing severity – been more explicit
Flynn report in the definition)

72. 2009 Pandemic Myths and Reality
Topic ? Believed Reality
Mortality Hardly anyone died – Reported deaths are a
only 2900 deaths in fraction of attributable
Europe additional deaths
Comparison with Was just like seasonal At least significant
seasonal influenza influenza or milder
Vaccination Nobody wanted to be Depends on the
experience vaccinated country
Pharmaceutical Drug companies had vast Unclear, what BMJ
company vaccine profits—$7bn to $10bn reported was an
profits from the from vaccines alone - arithmetically wrong
pandemic BMJ Editorial speculation of
potential sales