model

Report
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
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
Pi  1  exp[ βij ]
jI ( t )
M.E.J. Woolhouse, University of Edinburgh, August 2013
POST-EPIDEMIC SURVEILLANCE
MODEL
spatial microsimulation
+ non-detection risk
5x
System diagnostic sensitivity with increasing number of sheep
farms sampled showing risk-based sampling and random
selection from surveillance zone
M.E.J. Woolhouse, University of Edinburgh, August 2013
Handel et al. (2011) PLOS ONE
NETWORK MODEL FOR HAI
MODEL: stochastic, network SI
Hospital ID (ranked)
Patient movement network
Time to infection (yrs)
M.E.J. Woolhouse, University of Edinburgh, August 2013
Ciccolini et al. (submitted); van Bunnik et al. (in prep.)
NETWORK MODEL FOR HAI
Time to detection
Hospitals affected
RANDOM
GREEDY
6x
M.E.J. Woolhouse, University of Edinburgh, August 2013
8x
Ciccolini et al. (submitted); van Bunnik et al. (in prep.)
NETWORK MODEL FOR HAI
Time to detection (days)
STRAIN COMBINATION MODEL: MULTI-DRUG RESISTANCE
SINGLE
DOUBLE
No. hospitals
M.E.J. Woolhouse, University of Edinburgh, August 2013
van Bunnik et al. (in prep.)
DETECTING HPAI IN POULTRY FLOCKS
P (OUTBREAK)
P (UNDETECTED)
MODEL:
Within-flock IBM
+ background mortality
Probability of event
P (+ SENTINELS)
P (UNDETECTED)
P (+ SENTINELS)
Fraction birds protected
M.E.J. Woolhouse, University of Edinburgh, August 2013
Savill et al. (2006) Nature
DETECTING OUTBREAKS
AGAINST A BACKGROUND
PANDEMIC INFLUENZA IN SCOTLAND 2009
M.E.J. Woolhouse, University of Edinburgh, August 2013
OUTBREAK DETECTION
DATA: Spatial WCR
MODEL: Spatial IBM
+
Spatially explicit
simulations: allocate
cases to GPs by
postcode given set
probability of
reporting
Ferguson et al. (2006) Nature
M.E.J. Woolhouse, University of Edinburgh, August 2013
Singh et al. (2010) BMC Publ Hlth
OUTBREAK DETECTION
Case
Case reporting
reproting
rate
rate
Threshold
method
Cusum
method
specificity = 95%
WCR
THRESHOLD
WCR
method
0.5%
Sen
MDT
100
5
100
5
96
6
1.0%
Sen
MDT
100
4
100
5
97
5
5.0%
Sen
MDT
100
3
100
4
97
4
specificity = 99%
CUSUM
M.E.J. Woolhouse, University of Edinburgh, August 2013
0.5%
Sen
MDT
98
5
100
6
77
6
1.0%
Sen
MDT
100
5
100
5
92
6
5.0%
Sen
MDT
100
4
100
4
95
5
Singh et al. (2010) BMC Publ Hlth
DETECTING PANDEMIC INFLUENZA 2009
12 wks
What went wrong?
12 wks
Asynchronous outbreaks
Low R0
13 wks
M.E.J. Woolhouse, University of Edinburgh, August 2013
Singh et al. (2010) BMC Publ Hlth
SERO-SURVEILLANCE:
EXPOSURE VS VACCINATION
MODEL: age-time varying λ (MCMC fit)
1 in 3 people vaccinated already exposed
M.E.J. Woolhouse, University of Edinburgh, August 2013
McLeish et al. (2011) PLOS ONE
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
VIZIONS
Wellcome 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
VIET NAM HOSPITAL DATA
~250,000 infectious disease admissions
over 5 years
S.
N.
pneumoniae
meningitidis
6%
1%
Others
H.i.b
1%
6%
UNKNOWN
AETIOLOGIES
JEV 23%
49%
Co-infection
TBM
Dengue
HSV virus
2%
2%
2%
Dak Lak: dengue-like
fevers
Enteroviruses
2%
6%
M.E.J. Woolhouse, University of Edinburgh, August 2013
OUTBREAK IDENTIFICATION ALGORITHMS
M.E.J. Woolhouse, University of Edinburgh, August 2013
Bogich et al. (2011) Interface
RISK (NOT DISEASE) MAPPING
Institute of Medicine (2009)
Chan et al. (2010) PNAS
M.E.J. Woolhouse, University of Edinburgh, August 2013
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
Pi  1  exp[ βij ]
jI ( t )
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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