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Mark Woolhouse and many others Epidemiology Research Group

THEORY AND PRACTICE OF INFECTIOUS DISEASE SURVEILLANCE. Mark Woolhouse and many others Epidemiology Research Group Centre for Immunity, Infection & Evolution University of Edinburgh. M.E.J. Woolhouse, University of Edinburgh, August 2013. TOWARDS ‘SMART ’ SURVEILLANCE.

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Mark Woolhouse and many others Epidemiology Research Group

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  1. THEORY AND PRACTICE OF INFECTIOUS DISEASE SURVEILLANCE Mark Woolhouse and many others Epidemiology Research Group Centre for Immunity, Infection & Evolution University of Edinburgh M.E.J. Woolhouse, University of Edinburgh, August 2013

  2. TOWARDS ‘SMART’ SURVEILLANCE Using information on patterns of risk of infection to design more efficient (= less effort, lower cost) surveillance systems • Topics • Targeted surveillance: FMD, HAIs • Noisy backgrounds: influenza • Unusual events: EIDs • Theme • Model-based approaches to designing surveillance systems M.E.J. Woolhouse, University of Edinburgh, August 2013

  3. 5x System diagnostic sensitivity with increasing number of sheep farms sampled showing risk-based sampling and random selection from surveillance zone POST-EPIDEMIC SURVEILLANCE MODEL spatial microsimulation + non-detection risk Handel et al.(2011) PLOS ONE M.E.J. Woolhouse, University of Edinburgh, August 2013

  4. NETWORK MODEL FOR HAI MODEL: stochastic, network SI Patient movement network Hospital ID (ranked) Time to infection (yrs) Ciccoliniet al. (submitted); van Bunnik et al. (in prep.) M.E.J. Woolhouse, University of Edinburgh, August 2013

  5. NETWORK MODEL FOR HAI 6x 8x Time to detection Hospitals affected RANDOM GREEDY Ciccoliniet al. (submitted); van Bunnik et al. (in prep.) M.E.J. Woolhouse, University of Edinburgh, August 2013

  6. NETWORK MODEL FOR HAI STRAIN COMBINATION MODEL: MULTI-DRUG RESISTANCE Time to detection (days) SINGLE DOUBLE No. hospitals van Bunnik et al. (in prep.) M.E.J. Woolhouse, University of Edinburgh, August 2013

  7. DETECTING HPAI IN POULTRY FLOCKS P (OUTBREAK) P (UNDETECTED) P (UNDETECTED) P (+ SENTINELS) P (+ SENTINELS) MODEL: Within-flock IBM + background mortality Probability of event Fraction birds protected Savill et al. (2006) Nature M.E.J. Woolhouse, University of Edinburgh, August 2013

  8. DETECTING OUTBREAKS AGAINST A BACKGROUND PANDEMIC INFLUENZA IN SCOTLAND 2009 M.E.J. Woolhouse, University of Edinburgh, August 2013

  9. OUTBREAK DETECTION Ferguson et al. (2006) Nature DATA: Spatial WCR MODEL: Spatial IBM Spatially explicit simulations: allocate cases to GPs by postcode given set probability of reporting + Singh et al. (2010) BMC Publ Hlth M.E.J. Woolhouse, University of Edinburgh, August 2013

  10. OUTBREAK DETECTION Case reporting rate WCR THRESHOLD CUSUM Singh et al. (2010) BMC Publ Hlth M.E.J. Woolhouse, University of Edinburgh, August 2013

  11. DETECTING PANDEMIC INFLUENZA 2009 12 wks What went wrong? 12 wks Asynchronous outbreaks Low R0 13 wks Singh et al. (2010) BMC Publ Hlth M.E.J. Woolhouse, University of Edinburgh, August 2013

  12. SERO-SURVEILLANCE: EXPOSURE VS VACCINATION MODEL: age-time varying λ (MCMC fit) 1 in 3 people vaccinated already exposed McLeish et al.(2011) PLOS ONE M.E.J. Woolhouse, University of Edinburgh, August 2013

  13. DETECTING PANDEMIC INFLUENZA • Better data • More GPs (now 100s) • More frequent reporting (daily) • More reliable reporting • Serosurveillance data • Better pandemic models • Cleverer algorithms M.E.J. Woolhouse, University of Edinburgh, August 2013

  14. VIZIONSWellcome Trust-Viet Nam Initiative on Zoonotic Infections • AIMS: • Disease burden in a) hospital patients, b) high risk cohort • Outbreak detection algorithms • Identify drivers for disease emergence • Phylodynamics across species barriers • Bioinformatics methodologies M.E.J. Woolhouse, University of Edinburgh, August 2013

  15. VIET NAM HOSPITAL DATA ~250,000 infectious disease admissions over 5 years DakLak: dengue-like fevers M.E.J. Woolhouse, University of Edinburgh, August 2013

  16. OUTBREAK IDENTIFICATION ALGORITHMS Bogichet al.(2011) Interface M.E.J. Woolhouse, University of Edinburgh, August 2013

  17. RISK (NOT DISEASE) MAPPING Institute of Medicine (2009) Chan et al. (2010) PNAS M.E.J. Woolhouse, University of Edinburgh, August 2013

  18. CONCLUSIONS: BEING SMART • Risk is heterogeneous → targeting works • Smart surveillance is more efficient • More efficient post-epidemic FMD surveillance √ • Faster detection of HAIs √ • Faster outbreak detection? • Detection of novel infections/outbreaks? • Designing better surveillance systems is a challenging problem for modellers • More efficient surveillance and more effective interventions M.E.J. Woolhouse, University of Edinburgh, August 2013

  19. ACKNOWLEDGEMENTS Steve Baker (OUCRU), Paul Bessell, Marc Bonten (UTRECHT), Mark Bronsvoort, Bill Carman (NHSS), Margo Chase-Topping, Mariano Ciccolini, Peter Daszak (NEW YORK), T. Donker(GRONINGEN), Giles Edwards (SMRL), Jeremy Farrar (OUCRU), Neil Ferguson (IC), Eric Fèvre, Cheryl Gibbons,Ian Handel, Shona Kerr, Nigel McLeish, Jim McMenamin(HPS), Maia Rabaa, Chris Robertson (STRATHCLYDE), Nick Savill, Peter Simmonds, Brajendra Singh, Suzanne St Rose, Bram van Bunnik + Foresight and IOM/NAS committees, Generation Scotland FUNDING: Wellcome Trust, EC FP7, ICHAIR, SG, DEFRA, SFC, USAID, SIRN M.E.J. Woolhouse, University of Edinburgh, August 2013

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