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Statistics in Epidemics & Pandemics: The 2009 “Swine Flu” Pandemic

Statistics in Epidemics & Pandemics: The 2009 “Swine Flu” Pandemic. Azra Ghani MRC Centre for Outbreak Analysis & Modelling Department of Infectious Disease Epidemiology Imperial College London. Pandemic preparedness 2004-. H5N1 – first discovered 1997, but then spread widely from 2004.

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Statistics in Epidemics & Pandemics: The 2009 “Swine Flu” Pandemic

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  1. Statistics in Epidemics & Pandemics: The 2009 “Swine Flu” Pandemic Azra Ghani MRC Centre for Outbreak Analysis & Modelling Department of Infectious Disease Epidemiology Imperial College London

  2. Pandemic preparedness 2004- • H5N1 – first discovered 1997, but then spread widely from 2004. • Highly pathogenic to birds, esp. poultry. • and to people – 60% case fatality (507 cases, 302 deaths). • Even small chance of H5N1 pandemic warrants preparedness.

  3. Outbreak response:understand the threat • How bad is it? • How fast is it spreading? / How far has it got? • What can we do?

  4. Transmissibility

  5. How Fast is it Spreading? • Two quantities determine the speed of spread: • R0, basic reproduction number - average number of new infections generated by one primary infection • Tg, generation time – the average time from infection to passing infection on to another person

  6. Transmissibility Estimates Did fairly well for H1N1– estimated R0, origin date, extent of spread and upper bound on severity within 3 weeks. Fraser et al, Science 2009

  7. Difficulty in making predictions Serology? • Models fitted June-July increase well, and decline after schools closed. • But needed to know true number of infections to make predictions. • Still trying to assess what really happened.

  8. Severity

  9. Assessing severity • Case-finding misses most mild infections, giving uncertain denominator. • Early estimates from Mexico indicated mild-moderate severity – 0.04%-0.4% • Now estimate at ~1 death per 10,000 true infections. V mild Severe

  10. Planning Estimates • Confusion over the use of “reasonable worst-case scenarios” • Planning estimates used the upper 95% confidence bound on the case fatality ratio based on deaths/reported cases • Also gave “estimates” of epidemic trajectory

  11. But also some excellent reporting! Swine flu death rate estimates 'flawed' "We don't know the denominator," says Azra Ghani, head of a team at Imperial College London tracking development of the epidemic in the UK. "For that reason, dividing the number of deaths by the number of cases may be flawed," says Ghani's colleague Tini Garske, the lead author of the study exposing gaps in the data.

  12. Lessons learned

  13. Data issues ?? • Denominators - not knowing infection rates made estimation of severity and prediction of peak difficult: • Serosurveillance • Web/telephone cohorts

  14. Lessons learned from 2009 • Statistical analysis and modelling was useful for pandemic preparedness and response. • But not a crystal ball, or substitute for data. • Need to track community infections or illness to assess severity, predict peak. • Challenge to communicate changing uncertainty and severity estimates. • Lucky 2009 H1N1 was mild – can learn lessons. • But should not assume we’ll be so lucky next time – H5N1 still with us.

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