1. Issues and strategies in ex-post evaluation of intervention
against animal disease outbreaks and spreads
Mohamadou Fadiga & Hikuepi Katjiuongua
Mainstreaming Livestock Value Chain Conference.
5-6 Nov. 2013. Accra, Ghana

2. Impacts of animal diseases
• Animal disease outbreaks: can be devastating
Direct costs
- Death of animals
-
Lower productivity – slow growth, reduced efficiency of input use
- Control costs
Indirect costs
-
Reduced access to markets
Long term macroeconomic effects
Effects of price changes on supply chain actors
Spillover effects such as effects on tourism
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3. Impacts of animal disease
• Highly pathogenic avian influenza: Nigeria (2006/2008) 1.5 million
birds lost (747,000 culled)
• Foot and mouth disease: Botswana - trade ban led to $ 33 million
(USD) losses at processing level alone
• Tick & tick borne diseases: undermine livestock productivity
• Impacts differ by production system, coping & risk management
capacity of VC actors, state of veterinary service delivery
• Require costly interventions: culling, quarantine & movement
restrictions, and vaccinations…
• Important to evaluate animal disease intervention
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4. Issues in ex post evaluation of animal disease outbreaks
Four key issues in ex post economic assessment of
intervention against animal disease outbreaks
Defining the counterfactual scenario
Accounting for the losses (under counterfactual)
Handling data uncertainty and specificity of
epidemiological data
Dealing with the issues of attribution
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5. Issues in ex post evaluation of animal disease outbreaks
• Governments are generally risk averse with respect to animal
diseases
• Design intervention interventions to minimize losses in
expected social welfare
EW
i
p ( i )WD
i
[1 p( i )]WF
i
r( i )
• or some expected damage function
ED
i
DC ( i ) IC
i
• Measuring these losses accurately requires integrated
epidemiology and economic models
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6. On the counterfactual scenario
• Probability of outbreak is a composite risk estimate
– A product of or risk of introduction, risk of spread, and mortality risk
– Defining the counterfactual risk is akin to answering the question about what
would have been the trajectory of the disease in the absence of intervention
– Use a combination of experimental, historical, participatory data on the
disease, etc…
•
Use total death relative to population at risk to derive mortality risk
• For risk of spread
– Develop an SI (susceptible-infected)model
– Data on transmission rate, incubation period, infectious period, and lapse
between depopulation and restocking are used to solve the differential
equations that illustrate the SI model
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7. On the counterfactual scenario
Prevalence
50%
Endemic state
40%
30%
St
Ct
It
Rt
1 1
1
0
0
St 1
Ct 1
1
0
1 1
1
0
1 1
It 1
Rt 1
0
0
0
0
1
20%
Unstable epidemic
10%
0%
0
100
200
300
400
Day
Scenario
Spread
Risk Estimates
Mortality
Spread
Mortality
Composite
Burn-out
Low
0.13
0.01
0.0013
Burn-out
High
0.13
0.02
0.0026
Endemic
Low
0.27
0.01
0.0027
Endemic
High
0.27
0.02
0.0054
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8. Use of counterfactuals and example from HPAI in Nigeria
Calculate expected welfare or
expected damage without the
intervention at various scenarios
Net Social Welfare Gain over 2006-2010
US $ Million
Analyse the net effect of risk
reduction on social welfare
110
88
66
Find the socially optimal level of risk
that justifies intervention
44
22
0
0%
Evaluate if eradication makes sense
economically
20%
40%
60%
80%
100%
Risk Reduction Level
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9. Stochastic approach
• Characteristics of the data
– Underlying uncertainty (multiple sources, measurement errors, etc...)
– Disease transmission is stochastic
– Not all susceptible subjects in contact with infectious ones would
catch the disease
– Necessity to test the validity of results through sensitivity analysis
• Stochastic simulation addresses all these points at once
– Use the collected and/or derived data on spread and mortality to
simulate their distribution
– Solve for the key output variables using random draws from the
distribution of risk parameters
– Generate a distribution of the key output variables
– Conduct a probabilistic sensitivity analysis on the key output variables
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10. Stochastic simulation
Examples of Stochastic Results
Simulated Risks of HPAI
Welfare
Simulated Risk of Spread
18%
Losses
Frequency
15%
12%
Disease Cost
Mean
147.44
144.97
St. Deviation
291.93
115.99
Lower 95%
121.85
134.80
Upper 95%
173.03
155.14
9%
6%
3%
0%
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
Risk of Spread
Simulated Mortality Risk
Sensitivity analysis on welfare losses
53%
1.0
Probability
Frequency
45%
38%
30%
23%
15%
8%
0.8
0.6
0.4
0.2
0%
0.00
0.01
0.03
0.04
0.05
0.07
Mortality Risk
0.08
0.09
0.0
0
500
1,000
1,500
Social Welfare Effects
2,000
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11. Issues of attribution in intervention
• Ideal Scenario: randomization and use RCTs
• But scope for randomization nearly zero in case animal disease
outbreak – responding to a crisis. Alternative approaches exist but
depend on unit of analysis
• Other factors influence disease spread and the effectiveness of the
intervention (e.g. feed, water availability, agent behavior etc.)
• These make attribution of the intervention in a probabilistic sense
difficult
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12. Issues of attribution in intervention
• Necessity to use qualitative analysis in addition to quantitative assessment
targeting indicators through key informant interview about what could
have happened in the absence of the disease
• Use participatory epidemiology to capture indigenous knowledge about
the disease effects
• Pay attention to the timing of intervention and careful documentation of
all inputs used in the intervention and their sources
• Use past information about losses that coincided with the disease
outbreak to gain insights how the intervention may have potentially
affected the disease dynamics
• Overall: With that one could plausibly attribute observed change in
disease trajectory to the intervention that was carried out
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13. Accounting for losses
• Customary to focus on total cost (both direct and indirect)
• But when calculating total cost it is important to focus on
avoidable losses – can overestimate of cost savings
• In other words not all risk will be removed as a result of the
intervention
• Doing that would yield more reasonable incremental benefits
and incremental net benefits that accrued because of the
intervention
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14. Thank you
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