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Professor Angus Nicoll CBEEuropean Centre for Disease Prevention and ControlUsing and Developing Models for EpidemicInfect...
ECDC? What and Why A young independent EU agency dedicated to              the prevention and      control of communicable...
The role of ECDC? Identify, assess and communicate current  and emerging health threats to human   health from communicabl...
Declaration of Interests• No relevant commercial interests• Salary from government sources• Not a modeller• Some of my bes...
The three ages of a modelling developmentEnthusiasm – “Lets  model  it  …..”                 The solution (to all uncertai...
Variable Relationships between Science,Social Science and PolicyScience determining policiesvs:Science informing policiesv...
A worrying conversation        So  what’s           going to        happen?                       We really –          Oh ...
A worrying statement           Modelling           has shown            that  ….                Modelling                s...
So how was this talk prepared?I  have  worked  constantly  with  …  and asked modellers and policydevelopers / makersIt ha...
AcknowledgementsTommi AsaikainenJohn BeddingtonSimon CauchemezMarco CavaleriNeil FergusonPeter GroveDidier HoussinMaria va...
Plan of Talk•   An unusual talk about modelling•   Some limitations•   Some definitions•   Types of modellers and modellin...
Limitations of this talkThe uses of modelling in dealing with infections are legion! In  a short talk not dealing with the...
ModellingAlways a simplification                          13
Definition of modelling: 1. simple….a  construction  of  known  conceptual simplifications of anysystem under consideratio...
Definition – 2. more complex…..  a  simplified mathematical representation of acomplex process, device, or concept by mean...
ModellingAlways a simplification– so beware!                          16
“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…”         ...
Not all models are  mathematical                     19
The  point  is  ….Like there are many types of doctorsThere are many types of modellers and modelling…..  even just within...
Epidemic infections, influenza,and especially pandemic flu sodifficult….    Why?
The Need for SpeedAn example - SARS
The Metropole Hotel Hong Kong
Hotel M Floor                          Diagram                                               938             Lift for     ...
If the significance of the event could havebeen realised at the time, or at least earlier!Fortunately SARS was not soepide...
Complexity of transmission patternsMultiple interacting factors affect transmission patterns – so complexUnderstanding inf...
The ECDC Approach to flu, (andother epidemic respiratory diseases)Apologies to an American Sage
For any pandemic virus – what can andcannot be assumed?                                           What cannot be assumed:•...
For any pandemic virus – what can andcannot be assumed?                                           What cannot be assumed:•...
For any pandemic virus – what can andcannot be assumed?                                           What cannot be assumed:•...
The unexpected developments for 2009 :The unknown unknowns• The severe cases – with the severe cases being primary viral  ...
Not all of these are equally importantThe most important are thosethat can determine policy                               ...
Many successful examples ofmodelling…..  but  determining  policy?                                     34
Variable Relationships between Science,Social Science and PolicyScience determining policiesvs:Science informing policiesv...
Real-time outbreak analysis                                    • BSE/vCJD (1995) – estimates of                           ...
Models explain complexdynamics, can generate andsometimes even test hypothesesbut always need validation                  ...
Some Errors - Grove’s  Rules 1.   To believe the Modelling
It’s  not  magic……
Grove’s    Rules  1.   To believe the Modelling (that this will happen)  2.   Not to listen to the Modellers  3.   Not to ...
Communication CommunicationCommunication                              41
“One  version  of  the  truth”  Force the modellers to agree
“One  version  of  the  truth”  Don’t  introduce  modellers  at    different levels = chaos and lossof confidence
A danger – when the message frommodelling  is  ‘passaged’    - Stille Post                                             44
Early Mortality Estimates Tendto Be Higher than later                                 45
An example – where it can go wrong how many people are going to die from the pandemic in one country?What was estimated an...
How  the  ‘predictions’  evolved  – a EuropeanExample from the UK Modelling GroupEstimates were made from early on but in ...
Early influenza mortalityestimates tend to be higher thanlater…but  not  always                  48
Modest  but  tough  modellers  who  can  say  ‘No’  we do not know (yet) and understand policyconcernsEducated politicians...
What Helps – a lot                     50
Surveillance – Surveillance - SurveillanceThese data allow there to bevalidation and improvments ofestimates
The source of these data and analyses thatcome from them•   Confirmed cases•   Hospital and mortality based data•   Virolo...
The ECDC Approach AgainOperational Severity                          53
Surveillance – Surveillance - SurveillanceShould focus on information foraction
What are the Decisions?A decision to respondThe scale of the responseMitigation and infection controlMeasures to protect i...
Surveillance in a PandemicThe Parameters and RationalesStrategic Parameter                      Rationale for knowing     ...
Surveillance in a PandemicThe Parameters and RationalesStrategic Parameter                        Rationale for knowing   ...
Operational Aspects                      58
So then we have to/had to adapt generic plans to fitthe reality of any specific pandemic – operationalmodelling for option...
ECDC’s  Acid  Local  Tests1.Can local services robustly and effectively deliver  anti-virals to most of those that need th...
Conclusions – Modelling, Policies & AcuteInfections CrisesGood Things – Not so Good ThingsWhat modelling is good at with i...
Conclusions -2Groves RulesManaging expectations is keyLink to actionEducate the Policy MakersTry to get away from numbersC...
Selected Further ReadingVan Kerkhove MD, et al 2010 Studies Needed to Address Public Health Challenges of the 2009   H1N1 ...
Spare Slides               64
Modellers - a collective noun?a   crowd of people,a   flock of birds,a   mischief of mice,a   busyness of ferrets,a   farr...
Some difficult firsts of the 2009 pandemic 1The first pandemic to emerge in the twenty-first century. It has been more wid...
Some  difficult  ‘firsts’  - 2The first pandemic that took place within the context of a set of International Health Regul...
Some  difficult  ‘firsts’    - 3The first pandemic where intensive care was available in many countries to treat criticall...
2009 pandemic could have been a lotworse for Europe! (Situation April 2010)• A pandemic strain emerging in the Americas.  ...
2009 Pandemic Myths and RealityTopic                    ? Believed                    RealityWHO Statements           Talk...
2009 Pandemic Myths and RealityNicoll A, McKee M. Moderate pandemic, not many dead. Learning the right lessons in Europe f...
2009 Pandemic Myths and RealityTopic                ? Believed                RealityMortality            Hardly anyone di...
Using and Developing Models for Epidemic Infectious Disease Policy – Some examples from Influenza - Professor Angus Nicoll...
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Using and Developing Models for Epidemic Infectious Disease Policy – Some examples from Influenza - Professor Angus Nicoll CBE

  1. 1. Professor Angus Nicoll CBEEuropean Centre for Disease Prevention and ControlUsing and Developing Models for EpidemicInfectious Disease Policy– Some examples from InfluenzaModelling for Policy, The British Academy Conference May 17-18 2012, UK
  2. 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 citizens concerns
  3. 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. 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. 5. The three ages of a modelling developmentEnthusiasm – “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. 6. Variable Relationships between Science,Social Science and PolicyScience determining policiesvs:Science informing policiesvs:Science justifying pre-determined or cultural policiesvs:Science-free policies (which may include independent scientific activities in the countries)And what do we mean by Science?
  7. 7. A worrying conversation So  what’s   going to happen? We really – Oh don’t  know   dear Couldn’t   you model it? 7
  8. 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. 9. So how was this talk prepared?I  have  worked  constantly  with  …  and asked modellers and policydevelopers / makersIt has developed over time 9
  10. 10. AcknowledgementsTommi AsaikainenJohn BeddingtonSimon CauchemezMarco CavaleriNeil FergusonPeter GroveDidier HoussinMaria van KerkhoveMarianne van der SandeHelen Shirley-QuirkJacco WallingaPeter WhiteBut  the  views  and  opinions  are  mine  ….. 10
  11. 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. 12. Limitations of this talkThe 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. 13. ModellingAlways a simplification 13
  14. 14. Definition of modelling: 1. simple….a  construction  of  known  conceptual simplifications of anysystem under consideration whichcan then be analysedmathematically…..   14
  15. 15. Definition – 2. more complex…..  a  simplified mathematical representation of acomplex process, device, or concept by means of anumber of variables which are defined to represent theinputs, outputs, and internal states of the device orprocess, and by which something one understands, atheory,  can  be  applied  to  …..   15
  16. 16. ModellingAlways a simplification– so beware! 16
  17. 17. “for  every  complex,  difficult  problem there is frequently a solution that is simple, attractive…”
  18. 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. 19. Not all models are mathematical 19
  20. 20. The  point  is  ….Like there are many types of doctorsThere are many types of modellers and modelling…..  even just within public health and infectious diseasesModellers tend to specialiseSome specialise in:• Particular diseases or groups of diseases• Policy work• Networks analysis• Health Economics• Operational modelling – rarely….  and  much  more 20
  21. 21. Epidemic infections, influenza,and especially pandemic flu sodifficult….    Why?
  22. 22. The Need for SpeedAn example - SARS
  23. 23. The Metropole Hotel Hong Kong
  24. 24. Hotel M Floor Diagram 938 Lift for Flat Disabled Roof Persons Service room902 904 906 908 910 Lift Lift Hall 924 Hall 911 915 917 925 index 2 casesPreviously known Source case Study case cases
  25. 25. If the significance of the event could havebeen realised at the time, or at least earlier!Fortunately SARS was not soepidemic as influenza 26
  26. 26. Complexity of transmission patternsMultiple interacting factors affect transmission patterns – so complexUnderstanding infectious disease epidemiology requires modelling andanalysis to synthesise evidence from multiple sources • Contact patterns, % infections symptomatic, % seeking care, vaccine effectiveness, vaccine uptake, knowledge of the disease .→  Multidisciplinary:  needs  linked virologicalinformation, clinical, behavioural, biological,statistical, mathematical knowledge Example –Mexico 2009Modelling 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
  27. 27. The ECDC Approach to flu, (andother epidemic respiratory diseases)Apologies to an American Sage
  28. 28. For any pandemic virus – what can andcannot be assumed? What cannot be assumed:• What probably can be assumed: The known unknownsKnown 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
  29. 29. For any pandemic virus – what can andcannot be assumed? What cannot be assumed:• What probably can be assumed: The known unknownsKnown 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
  30. 30. For any pandemic virus – what can andcannot be assumed? What cannot be assumed:• What probably can be assumed: The known unknownsKnown 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
  31. 31. 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
  32. 32. Not all of these are equally importantThe most important are thosethat can determine policy 33
  33. 33. Many successful examples ofmodelling…..  but  determining  policy?   34
  34. 34. Variable Relationships between Science,Social Science and PolicyScience determining policiesvs:Science informing policiesvs:Science justifying pre-determined or cultural policiesvs:Science-free policies (which may include independent scientific activities in the countries)And what do we mean by Science?
  35. 35. 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 120Confirmed 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
  36. 36. Models explain complexdynamics, can generate andsometimes even test hypothesesbut always need validation 37
  37. 37. Some Errors - Grove’s  Rules 1. To believe the Modelling
  38. 38. It’s  not  magic……
  39. 39. 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
  40. 40. Communication CommunicationCommunication 41
  41. 41. “One  version  of  the  truth”  Force the modellers to agree
  42. 42. “One  version  of  the  truth”  Don’t  introduce  modellers  at    different levels = chaos and lossof confidence
  43. 43. A danger – when the message frommodelling  is  ‘passaged’    - Stille Post 44
  44. 44. Early Mortality Estimates Tendto Be Higher than later 45
  45. 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 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.eceDont 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
  46. 46. How  the  ‘predictions’  evolved  – a EuropeanExample from the UK Modelling GroupEstimates were made from early on but in private – early wide confidence limits - then a statement was madeJuly 17th 2009 range of - 3,100 to 65,000 deathsBy Sept 2009 For Winter – Autumn wave – Diagnosed and reported deaths: 70 deaths lower estimate 420 deaths upper estimate 840 deaths reasonable worse caseBy February 2010 – 242 deathsConclusion - try not to give estimates when there is a lot of uncertainty – especially the upper ones 47
  47. 47. Early influenza mortalityestimates tend to be higher thanlater…but  not  always   48
  48. 48. Modest  but  tough  modellers  who  can  say  ‘No’  we do not know (yet) and understand policyconcernsEducated politicians with some understandingof limits of modellingOr  a  ‘translator’   49
  49. 49. What Helps – a lot 50
  50. 50. Surveillance – Surveillance - SurveillanceThese data allow there to bevalidation and improvments ofestimates
  51. 51. The source of these data and analyses thatcome 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
  52. 52. The ECDC Approach AgainOperational Severity 53
  53. 53. Surveillance – Surveillance - SurveillanceShould focus on information foraction
  54. 54. What are the Decisions?A decision to respondThe scale of the responseMitigation and infection controlMeasures to protect individuals - prioritisationMeasures to reduce and slow transmissionInvestment allocationTiming of the responses 55
  55. 55. Surveillance in a PandemicThe Parameters and RationalesStrategic Parameter Rationale for knowing (what actions follow)Identify and monitor changing Provide timely and representativephenotypic / genotypic characteristics virological input data to WHOof 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 antiviralsBroad estimate of severity of the Determining the limits of public healthpandemic – ECDC Severity Matrix actions that are justified
  56. 56. Surveillance in a PandemicThe Parameters and RationalesStrategic Parameter Rationale for knowing (what actions follow)Confirm / determine case definition and Confirm or refine default case definitionits predictive value for offering testing / treatment (antivirals) To determine when laboratories can reduce the amount of confirmatory testing of casesGive relative estimates of incidence and Target interventions and refinedisease by age-group or other risk countermeasures e.g. who to giveparameters (e.g. those with chronic antivirals and human avian influenza andconditions, pregnant women) specific pandemic vaccines
  57. 57. Operational Aspects 58
  58. 58. So then we have to/had to adapt generic plans to fitthe reality of any specific pandemic – operationalmodelling for optionsNo battle plan ever survivescontact  with  the  enemy… ―  Field  Marshall  Helmuth Carl Bernard von Moltke, 1800–1891I.e. we had generic pandemicplans, but then we had to adaptthem to the specific featurespeculiar to this pandemic. Statue of Helmuth von Moltke the Elder, Berlin 59
  59. 59. ECDC’s  Acid  Local  Tests1.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
  60. 60. Conclusions – Modelling, Policies & AcuteInfections CrisesGood Things – Not so Good ThingsWhat modelling is good at with infections (may work):Planning – what might happenPost-event analyses – what did happenWhat needs to be determined – e.g. rapid seroepidemiologyWhat might workWhat certainly will not workIt  helps  if  the  disease  is  ‘slower  moving’    What is more challenging (probably  won’t  work):Use in the midst of the pandemic‘Forecasting  – predictingNow-casting’    - a spcial case 61
  61. 61. Conclusions -2Groves RulesManaging expectations is keyLink to actionEducate the Policy MakersTry to get away from numbersCommunicationsLink to Actions 62
  62. 62. Selected Further ReadingVan 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.htmlNicoll 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 2011Lipsitch 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
  63. 63. Spare Slides 64
  64. 64. 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
  65. 65. Some difficult firsts of the 2009 pandemic 1The 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.
  66. 66. Some  difficult  ‘firsts’  - 2The 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”.  
  67. 67. Some  difficult  ‘firsts’    - 3The 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
  68. 68. 2009 pandemic could have been a lotworse 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
  69. 69. 2009 Pandemic Myths and RealityTopic ? Believed RealityWHO Statements Talked up the pandemic Certainly did notPandemic Was not a pandemic at all Fitted all definitionsPandemic 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 deathsComparison with Was just like seasonal Similarities but at least 9seasonal influenza influenza or milder significant differencesVaccination experience Nobody wanted to be Depends on the country vaccinatedPharmaceutical Drug companies had vast Unclear, what BMJcompany vaccine profits—$7bn to $10bn reported was arithmeticallyprofits from the from vaccines alone - BMJ wrong speculation ofpandemic Editorial from Flynn Report potential sales
  70. 70. 2009 Pandemic Myths and RealityNicoll A, McKee M. Moderate pandemic, not many dead. Learning the right lessons in Europe from the 2009 pandemic EJPH October2010 vol. 20 no. 5 486-488 doi:10.1093/eurpub/ckq114 http://eurpub.oxfordjournals.org/content/20/5/486.full andhttp://eurpub.oxfordjournals.org/content/suppl/2010/09/27/ckq114.DC1/ckq114_suppl.pdfTopic ? Believed RealityWHO Talked up the Certainly did notStatements pandemicPandemic Was not a pandemic Fitted all at all definitionsPandemic WHO changed it to Certainly did notDefinition fit the pandemic – (but should have removing severity – been more explicit Flynn report in the definition)
  71. 71. 2009 Pandemic Myths and RealityTopic ? Believed RealityMortality Hardly anyone died – Reported deaths are a only 2900 deaths in fraction of attributable Europe additional deathsComparison with Was just like seasonal At least significantseasonal influenza influenza or milderVaccination Nobody wanted to be Depends on theexperience vaccinated countryPharmaceutical Drug companies had vast Unclear, what BMJcompany vaccine profits—$7bn to $10bn reported was anprofits from the from vaccines alone - arithmetically wrongpandemic BMJ Editorial speculation of potential sales

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