The European Commission for the Control of Foot-and-Mouth Disease (EuFMD), one of FAO’s oldest Commissions, came into being on the 12th June 1954, with the pledge of the sixth founding member state to the principles of a coordinated and common action against foot-and-mouth disease.

1. OS18
Investigating the benefits of an adaptive
management approach involving emergency
vaccination using simulated FMD outbreaks
in New Zealand
Robert Sanson, Zhidong Yu, Tom Rawdon, Mary van Andel

2. OS18
Objectives
• Investigate the strategic use of emergency FMD
vaccination via an adaptive management approach
• i.e. Only vaccinate if there are sufficient early decision
indicators that a large outbreak is developing
• Assess the predictability of these early decision
indicators (EDIs)

3. OS18
Background
• New Zealand has a model of FMD that has been
progressively refined and extended since 1993, using
InterSpread Plus (ISP)
• A number of modelling studies in New Zealand and
elsewhere have demonstrated the benefits of emergency
FMD vaccination, particularly during large outbreaks
• However there are time and cost penalties to restore
country disease status to FMD Free Without Vaccination
under current OIE rules if vaccination is used
• Therefore investigating the optimal use of vaccination to
augment Stamping Out

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Background (contd.)
• Recently collaborated with the QUADs countries to
explore the predictability of large outbreaks
simulated in our respective countries early in the
response (particularly first 3 weeks)
• Generated 10,000 random introductions of FMD into
each country
• Assessed a set of Early Decision Indicators (EDIs)
• Number of IPs at various time points
• Estimated Dissemination Rates (EDR)
• Number of geographical clusters and extents
• Density of humans and livestock populations around the index
farm etc.

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Early Decision Indicators (EDIs)
• From these analyses, selected two EDIs that could
be used within the ISP modelling system to act
together as a complex trigger to implement
vaccination if the defined thresholds were
exceeded:
• Estimated Dissemination Rates (EDR) calculated daily
• Where EDR = IPs t2 / IPs t1
• Cumulative number of IPs at various time points

6. OS18
EDI Trigger – defined thresholds
Complex Trigger
Time period of
the response
(days)
5-day EDR Cumulative IPs
11 - 14 >= 2.0 >= 20
15 - 21 >= 1.5 >= 25
22 - 28 >= 1.5 >= 29
29 - 35 >= 1.5 >= 32

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Methods
• 5000 random introductions into northern New
Zealand
• 4 broad strategies:
• Stamping Out only (SO)
• SO + Vaccination triggered by the complex EDI trigger
operating between days 11 - 35 of the response (TRV)
• SO + Vaccination randomly started between days 11 – 35
(VAC)
• SO + Vaccination started on Day 21 of the response
(VACf)

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Methods (contd.)
• Randomise a number of other parameters for each
iteration:
• Airborne spread on/off
• Numbers of personnel available for different tasks
• 6 personnel types, each varied randomly from 7 ~ 1000
• Radius of vaccination zones around IPs (1 – 5 km)
• Whether lifestyle farms (smallholders) are vaccinated
• Whether all farm types are vaccinated or only those
with cattle
• Number of traces investigated per person per shift
• Number of farms per day that a surveillance Vet can visit

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Outputs
• Type of Primary Case farm
• Density of livestock around Primary Case
• Time to first detection (days)
• If and when the EDI trigger fired (recorded for all
simulations, even though only used by TRV strategy
to modify the response)
• # doses of vaccine used
• # IPs
• Length of response (up to a maximum of 365 days)

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Statistical Analyses
• Descriptive and uni-variable
• Boxplots / Kruskal Wallis tests
• Multi-variable
• Logistic & negative binomial regression
• Machine Learning Tree partitioning approaches
• CART / Random Forests / Boosted Regression Trees
• Performance of the two complex EDI triggers via
2x2 contingency tables:
• Se / Sp / PPV / NPV

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Results
• 13 / 5000 (0.26%) where disease died out without
being detected
• 605 iterations (12.1%) where disease failed to
spread off Primary Case (single outbreaks)
• Highly skewed with extreme outliers due to
insufficient manpower resources
• Excluding the outliers:
• Mean IPs 30.4, Median 10, Range 1 – 751
• Mean duration 27.1 days, Median 19, Range 1 - 217

12. OS18
Negative Binomial Regression – significant variables
• IPs
• EDI Trigger fired
• Strategy (Vaccination protective but TRV best)
• Time to first detection
• Type of Primary Case farm
• Number of Livestock Technicians and Slaughtermen
• Duration
• EDI Trigger Fired
• Strategy (Vaccination protective but TRV best)
• Time to first detection
• Type of Primary Case farm
• # Farms that a Vet can visit per day

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Relative Benefits of Vaccination
IPs Duration
Kruskal Wallis p=0.049 Kruskal Wallis p<0.0001

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Doses used for Vaccination Strategies
Kruskal Wallis p<0.0001

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Performance of 5d EDI Trigger on IPs
IPs > 32
(lg)
IPs <= 32
(sm)
Totals
Trigger + 142 147 289
Trigger - 1 290 291
Totals 143 437 580
Sensitivity (Se) = 0.993
Specificity (Sp) = 0.664
Positive predictive value (PPV) = 0.491
Negative predictive value (NPV) = 0.997

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Performance of 5d EDI Trigger on Duration
> 40d
(long)
<= 40d
(short)
Totals
Trigger + 130 159 289
Trigger - 6 285 291
Totals 136 444 580
Se = 0.956
Sp = 0.642
PPV = 0.45
NPV = 0.979

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Conclusions
• An adaptive management approach to
implementing vaccination only when there was an
indication that a large outbreak was developing
(TRV) was the most effective strategy
• The EDI trigger was very sensitive to detecting large
outbreaks. NPV was also very high – which means
that if an outbreak was predicted to be small,
generally it turned out to be small.

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Conclusions (contd.)
• Examining the extreme outliers, # Vets available
was of crucial importance for managing large
outbreaks. If <= 43 Vets available, the disease could
become endemic in New Zealand
• Vaccinating only farms with cattle was no better or
worse than vaccinating all farm types
• Vaccinating Lifestyle / smallholder farms reduced
duration but not the number of IPs amongst the
‘moderate’ (expected) outbreaks

19. OS18
Acknowledgements
• The Ministry for Primary Industries (MPI) for
funding the project
• EpiSoft for allowing use of the InterSpread Plus
modelling system
• AsureQuality Limited for providing access to farm
and animal data from AgriBase