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Recent Real-Time experience in
decision making on FMD control
The Silver Birch Exercise (UK).
Part I: the Simulation Exercise;
Part II: The Decision support tools;
Part III: What have we learnt for the future?
Kate Sharpe
Francesca Culver
Rory O’Donnell
Part I: the Simulation Exercise
FMD Exercise Silver Birch
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Background to the exercise
Aim
Participants
International observers
Silver Birch Scenario - Themes
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Carcase disposal
Deployment of vaccination and resources
Animals at risk
Laboratory capacity, mobile testing equipment
Export and movement of livestock and livestock products
Movement standstills
Animal Welfare
Financial considerations
Meat and food chain issues
Animal Health resourcing issues
Rural community issues
Communications and policy with and between UK and the
Devolved Administrations
Silver Birch Scenario - Themes
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Carcase disposal
Deployment of vaccination and resources
Animals at risk
Laboratory capacity, mobile testing equipment
Export and movement of livestock and livestock products
Movement standstills
Animal Welfare
Financial considerations
Meat and food chain issues
Animal Health resourcing issues
Rural community issues
Communications and policy with and between UK and the
Devolved Administrations
Silver Birch Scenario-Assumptions
• On the dates of this Exercise there were no confirmed outbreaks
of FMD in the European Union.
• The international situation with regards to this disease is current
as for November 2010.
• With a few exceptions we used real farms and real data.
• Unless specifically outlined we assumed full compliance with
domestic legislation England, Wales and Scotland – including
animal identification, movement records and reporting, standstills,
animal gatherings etc.
Silver Birch Scenario
Part II: The Decision support tools
• The role of modelling as part of the
evidence base for disease control
Policy makers use evidence that
comes from a variety of sources
Risk
assessment
Industry &
other groups
Economic
Legal
Scientific
Modelling
Policy
makers
Political
Public
acceptability
Veterinary
Epidemiological
Anaylses
Risk
appetite
Where modelling fits
– Important part of the evidence for decision making
• To investigate how an epidemic may develop (no controls)
• Predict likely outcomes in different scenarios
– Must take other influences into account
• A control measure that cannot be implemented is not useful!
– Data availability
• Data for models also feeds other analyses to provide evidence
to policy makers
• Most cost effective to have common data for a range of
evidence requirements
Use of Models
• Epidemiology models (essential)
– Planning – how big an outbreak to plan for?
– Exercises – realistic scenarios
– Outbreaks – geographic spread, size, should we
vaccinate?, forward planning etc.
• Resource models (nice to have)
- Advice to policy on delivery constraints
- Informing decisions on ramping up (and down)
- Monitoring of actual vs predicted resources
• Integrated epidemiology & resource (ideal)
– Resource constraints (and time taken) informs the epi
modelling – otherwise can be unrealistic
Exodis-FMD™ model
• It explicitly models the current Defra Contingency Plan.
• Can be used to:
– Predict the shape and size of an FMD outbreak (farms infected,
farms culled, animals culled, animals vaccinated etc) in both space
and time
– compare different policy options
– test different control strategies in the event of an emerging outbreak
– Calculates the resources required to deliver these control strategies
– generate simulated outbreaks for use in training and readiness
exercises.
• Outputs can be fed into the Economic Consequences Model
Economic Consequences Model
• Exodis model run1000 times for each different
control strategy and scenario
• produces file of numbers that physically describe
the modelled outbreak
• Numbers feed into a spreadsheet called the
Economic Consequences Model (ECM)
• ECM values each of the physical aspects of the
outbreak, so calculates its total cost
• Compare total costs of outbreak (mean, median,
8th and 92nd percentile of the 1000 runs)
Exodis-FMD model - Data used :
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Farm census data – used to define the location and size of the farm population.
Animal movement data – used to define animal movement patterns.
Epidemiological data – used to define key virus response and infection
behaviour (likelihood of becoming infected or infecting others, incubation period,
infectious period, time to immunity after vaccination etc).
Emerging epidemic data – used to define the current state of an outbreak
(infected farms, infection date, source farm, diagnosis date, slaughter date,
number of stock, DC culls, vaccinated farms and animals etc). DCS data is
required during an emerging outbreak.
Control policy choices – size of control zones, culling and vaccination policy
and local implementation strategy etc.
Resource data – used to define how much resource is required for each task,
and how much resource is available regionally and nationally over the course of
an outbreak. Resource parameterization is supplied by Defra and Animal Health.
Wind data – used to define the likely direction and spread of long-range virus
plumes
Models can be data-hungry
NAME (Nuclear Accident MoDel)
• Atmospheric dispersion model
• coupled with a new risk prioritisation model can
be used to identify the farms at greatest risk of
airborne infection from virus emitted from the
notified outbreaks.
Epidemiological modelling in the exercise
Should we vaccinate?
EU directive on Community measures for the control of
foot-and-mouth disease (2003/85/EC) states
‘That the competent authority shall, immediately upon
confirmation of the first outbreak of foot-and-mouth
disease prepare all arrangements necessary for
emergency vaccination in an area of at least the size of
the surveillance zone.’
(UK policy is vaccinate to live)
Measuring the effect
• Aims of vaccination as part of FMD
control measures
– To reduce final number of IPs?
– To reduce the number of animals culled?
– To reduce length of epidemic?
Exodis-FMD – Exercise Assumptions
Vaccination treatment:
• Vaccination starts on day 10. Cattle only
• Vaccination teams based on contract with Genus = 50 teams at day 5, 100 at day
10, 150 at day 21.
• 50 available only in Cheshire and North Leeds from day 10, 100 available
throughout GB from day 13.
• Maximum 240 animals / team / day = maximum 36,000 animals / day
• Vaccine 90% effective after 4-6 d. No effect before.
• Vaccine protects until end of outbreak
• Transmission and infection proportional to number of susceptible animals
• Prioritised by susceptibility = herd size
Assumed virus traits:
• The virus strain modelled being similar to 2001 FMD outbreak strain (and
especially how it behaves in sheep)
• Clinical signs in sheep very difficult to detect
• No detection in peace time
• No detection of first infected animal
• A chance of detecting general flock infection during an outbreak, which is highest
close to IPs.
Exodis-FMD – Silver Birch outputs
Impact of vaccination on IPs in England, Scotland and Wales
600
500
400
300
IPs
Impact of vaccination
on number of IPs
(given as a range
following 1000 runs of
the model on the
scenario)
92 percentile
200
Mean
Median
100
8 percentile
0
Not vaccinated
Vaccinated
Not vaccinated
England
Note that vaccination has
large impacts on large
outbreaks in Wales and
Scotland, but the numbers
of IPs in Wales and
Scotland are very low in
over half of outbreaks,
whether or not vaccination
is used.
England
Scotland
Wales
Not vaccinated
Scotland
Mean
Not
vaccinated
Vaccinated
Not
vaccinated
Vaccinated
Not
vaccinated
Vaccinated
Vaccinated
Vaccinated
Wales
8%
50%
92%
134.5
102.6
46
46.5
114.5
89.5
248.5
174.5
19.3
8.2
1
1
1
1
42
4
125
37.9
1
1
8.5
9
507
157
Exodis-FMD – Silver Birch outputs
Economic analysis of FMD vaccination
Outbreak cost in Silver Birch example
(assumes vaccinated cattle lose 10% value)
Outbreak cost in Silver Birch example
(assumes vaccinated cattle lose 50% value)
Modelling and economic conclusions
from Silver Birch
• The modelling and economic paper that was prepared
during Exercise Silver Birch concluded that:
– Vaccination may have reduced the expected outbreak cost, although
loss of value of vaccinated animals could alter this (small increase in
average outbreak cost)
– Longer export ban is a less important factor (vaccination shortens the
outbreak)
– Vaccination would substantially reduce the expected (average)
number of animals culled and the number of farms with culls
Part III: What have we learnt for the future?
• Decision to vaccinate taken at Ministerial level.
• Not just a simple yes or no
• Need to decide and consider:
– When, where, which species
– Can it be implemented without stopping other
control measures?
– What controls and requirements will be required on
vaccinated aniimals
– Industry, consumer and political perception
– Exit strategy
Preparedness
• IAH-Pirbright able to identify the virus strain
within 6 hours of receipt.
• Contracts to produce up to 2.5m doses of
strain specific vaccine
• Available within 4 days from the UK vaccine
bank.
• Contracts with Genus Plc, to start vaccine
deployment within 5 days.
Policy makers use evidence that
comes from a variety of sources
Risk
assessment
Industry &
other groups
Economic
Legal
Scientific
Modelling
Policy
makers
Political
Public
acceptability
Veterinary
Epidemiological
Anaylses
Risk
appetite
When to vaccinate
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Must be deployed at the right time:
– Generally 4 days after vaccination there are good levels of protection in
animals which rises to very good levels after 7 days.
•Too soon:
– as yet undetected disease could appear beyond the vaccination zone,
seriously jeopardising the disease control policy and meaning vaccine would
have been wasted.
– IPs could appear in an area where vaccination would have a bigger impact
than where originally deployed.
Too late:
– the spread of disease would have been such that vaccinating would have little
or no impact on disease control.
NB: While ideally a vaccination campaign might take place against a backdrop of a
clear epidemiological picture, this is not necessarily possible in the early stages of
an outbreak when disease information may be incomplete.
Veterinary resources needed
Vaccination also:
• Significantly increases operational complexity
• Significantly increases the resources needed
• Is only likely to be effective if you have a clear picture
of where and when you will use it and all involved are
committed to delivering it effectively
In conclusion
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• Silver Birch was a very useful exercise.
• Allowed us to explore the use of our models as decision
support tools
• Issues identified have lead to further work on:
Exploring alternative vaccination scenarios e.g.
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Reducing size of vaccination zone from 10km to 7km/5km
Vaccinating in specific geographical areas only
vaccination in species other than cattle.
Different prioritisation of herds or outside-in approach.
To enable more discussions with stakeholders