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

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

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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

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)

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

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

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

DETECTING OUTBREAKS

M.E.J. Woolhouse, University of Edinburgh, August 2013

AGAINST A BACKGROUND

PANDEMIC INFLUENZA IN SCOTLAND 2009

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

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

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

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

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

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

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

Bogich et al. (2011) Interface

OUTBREAK IDENTIFICATION ALGORITHMS

M.E.J. Woolhouse, University of Edinburgh, August 2013

RISK (NOT DISEASE) MAPPING

Institute of Medicine (2009)

M.E.J. Woolhouse, University of Edinburgh, August 2013

Chan et al. (2010) PNAS

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

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M.E.J. Woolhouse, University of Edinburgh, August 2013

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