mark woolhouse and many others epidemiology research group
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
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Mark Woolhouse and many othersEpidemiology Research Group
Centre for Immunity, Infection & EvolutionUniversity of Edinburgh
THEORY AND PRACTICE OF INFECTIOUS DISEASE SURVEILLANCE
M.E.J. Woolhouse, University of Edinburgh, August 2013
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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
TOWARDS ‘SMART’ SURVEILLANCE
M.E.J. Woolhouse, University of Edinburgh, August 2013
)(
]βexp[1tIj
ijiP
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POST-EPIDEMIC SURVEILLANCE
Handel et al. (2011) PLOS ONE
System diagnostic sensitivity with increasing number of sheep farms sampled showing risk-based sampling and random
selection from surveillance zone
MODEL
spatial microsimulation
+ non-detection risk
5x
M.E.J. Woolhouse, University of Edinburgh, August 2013
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NETWORK MODEL FOR HAI
Ciccolini et al. (submitted); van Bunnik et al. (in prep.)
Patient movement network
MODEL: stochastic, network SI
M.E.J. Woolhouse, University of Edinburgh, August 2013
Time to infection (yrs)H
ospi
tal I
D (r
anke
d)
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NETWORK MODEL FOR HAI
M.E.J. Woolhouse, University of Edinburgh, August 2013 Ciccolini et al. (submitted); van Bunnik et al. (in prep.)
RANDOM
GREEDY
6x 8x
Hospitals affectedTime to detection
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NETWORK MODEL FOR HAI
M.E.J. Woolhouse, University of Edinburgh, August 2013 van Bunnik et al. (in prep.)
STRAIN COMBINATION MODEL: MULTI-DRUG RESISTANCETi
me
to d
etec
tion
(day
s)
SINGLE
DOUBLE
No. hospitals
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P (+ SENTINELS)
P (OUTBREAK)
P (UNDETECTED)
P (UNDETECTED)
P (+ SENTINELS)
DETECTING HPAI IN POULTRY FLOCKS
Savill et al. (2006) Nature
MODEL:
Within-flock IBM
+ background mortality
M.E.J. Woolhouse, University of Edinburgh, August 2013
Fraction birds protected
Prob
abili
ty o
f eve
nt
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DETECTING OUTBREAKS
M.E.J. Woolhouse, University of Edinburgh, August 2013
AGAINST A BACKGROUND
PANDEMIC INFLUENZA IN SCOTLAND 2009
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OUTBREAK DETECTION
+
Singh et al. (2010) BMC Publ Hlth
Ferguson et al. (2006) Nature
Spatially explicit simulations: allocate cases to GPs by postcode given set probability of reporting
MODEL: Spatial IBMDATA: Spatial WCR
M.E.J. Woolhouse, University of Edinburgh, August 2013
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Case reproting
rate
WCR method
Threshold method
Cusum method
Sen 100 100 96MDT 5 5 6
Sen 100 100 97MDT 4 5 5
Sen 100 100 97MDT 3 4 4
Sen 98 100 77MDT 5 6 6
Sen 100 100 92MDT 5 5 6
Sen 100 100 95MDT 4 4 5
specificity = 99%
0.5%
1.0%
5.0%
0.5%
1.0%
5.0%
specificity = 95%
OUTBREAK DETECTION
WCR
CUSUM
THRESHOLD
Singh et al. (2010) BMC Publ HlthM.E.J. Woolhouse, University of Edinburgh, August 2013
Case reporting rate
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DETECTING PANDEMIC INFLUENZA 2009
What went wrong?
Asynchronous outbreaks
Low R0
Singh et al. (2010) BMC Publ HlthM.E.J. Woolhouse, University of Edinburgh, August 2013
12 wks
12 wks
13 wks
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SERO-SURVEILLANCE: EXPOSURE VS VACCINATION
McLeish et al. (2011) PLOS ONE
1 in 3 people vaccinated already exposed
MODEL: age-time varying λ (MCMC fit)
M.E.J. Woolhouse, University of Edinburgh, August 2013
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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
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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
VIZIONSWellcome Trust-Viet Nam Initiative on Zoonotic Infections
M.E.J. Woolhouse, University of Edinburgh, August 2013
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VIET NAM HOSPITAL DATA
M.E.J. Woolhouse, University of Edinburgh, August 2013
H.i.b6%
S. pneu-moniae
6%N. meningi-
tidis1%
Others1%
JEV 23%
Dengue virus2%HSV
2%Enteroviruses6%
TBM2%
Co-infection2%
UNKNOWN AETIOLO-
GIES49%
Dak Lak: dengue-like fevers
~250,000 infectious disease admissions over 5 years
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Bogich et al. (2011) Interface
OUTBREAK IDENTIFICATION ALGORITHMS
M.E.J. Woolhouse, University of Edinburgh, August 2013
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RISK (NOT DISEASE) MAPPING
Institute of Medicine (2009)
M.E.J. Woolhouse, University of Edinburgh, August 2013
Chan et al. (2010) PNAS
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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
)(
]βexp[1tIj
ijiP
M.E.J. Woolhouse, University of Edinburgh, August 2013
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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