Professor Angus Nicoll CBE European Centre for Disease Prevention and Control

1. 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

2. 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

3. 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

4. Declaration of Interests • No relevant commercial interests

5. Declaration of Interests • No relevant commercial interests • Salary from government sources

6. Declaration of Interests • No relevant commercial interests • Salary from government sources • Not a modeller

7. Declaration of Interests • No relevant commercial interests • Salary from government sources • Not a modeller • Some of my best friends are modellers

8. 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 ! * !

9. 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

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

11. A worrying statementmodelling generates hypothesesidentifies, quantifies uncertainty, tells you what to look for test hypotheses Modelling has shown that …. Modelling suggests that

12. So how was this talk prepared?

13. So how was this talk prepared? • I asked modellers and policy developers / makers

14. 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 …..

15. 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

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

17. 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 …..

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

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

20. Not all models are mathematical

21. Modellers - a collective noun?

22. Modellers - a collective noun? • a crowd of people,

23. Modellers - a collective noun? • a crowd of people, a flock of birds,

24. Modellers - a collective noun? • a crowd of people, a flock of birds, a mischief of mice,

25. Modellers - a collective noun? • a crowd of people, a flock of birds, a mischief of mice, a busyness of ferrets,

26. Modellers - a collective noun? • a crowd of people, a flock of birds, a mischief of mice, a busyness of ferrets, a farrow of pigs,

27. 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 distributionof modellers??

28. 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

29. Why is flu, and especially pandemic flu so difficult

30. 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 The complexity of transmission patterns

31. For any pandemic virus – what can and cannot be assumed? • 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 • 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

32. For any pandemic virus – what can and cannot be assumed? • What cannot be assumed: • Theknown 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 • 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

33. For any pandemic virus – what can and cannot be assumed? • 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 • 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

34. Many successful examples of modelling

35. Real-time outbreak analysis • BSE/vCJD (1995) – estimates of exposure, modelling of risk-reduction. • UK Foot and Mouth Disease epidemic (2001) – modelling guided control policy. • SARS 2003 – estimates of transmissibility (R0~3) and CFR (~15%).

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

37. Some Errors - Grove’s Rules • To believe the Modelling

38. It’s not magic……

39. Two Errors or Grove’s Rules • To believe the Modelling

40. Two Errors – Grove’s Rules • To believe the Modelling • Not to listen to the Modellers

41. A third Error – Grove’s Rules • To believe the Modelling • Not to listen to the Modellers • Not to seek validation – surveillance data

42. Communication CommunicationCommunication

43. One version of the truth • Force the modellers to agree • Don’t introduce them at different levels

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

45. 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.

46. 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

47. Modest but tough modellers who can say ‘No’ and understand policy concernsEducated politicians with some understanding of limits of modellingOr a ‘translator’

48. But so what?

49. Surveillance – Surveillance - Surveillance

50. Surveillance – Surveillance - Surveillance • Should be information for action