decision support framework for managing rift valley fever in the horn of africa
DESCRIPTION
Presentation by Jeffrey C. Mariner, Bernard Bett and John Gachohi at a workshop on Rift Valley fever: Challenge, prevention and control held at Mombasa, Kenya on 13-15 November 2012.TRANSCRIPT
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Decision support framework
for managing Rift Valley fever
in the Horn of Africa
Jeffrey C. Mariner, Bernard Bett and John Gachohi
Presentation at a workshop on Rift Valley fever: Challenge, prevention and control,
Mombasa, Kenya, 13-15 November 2012.
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Overview
• ILRI RVF Research Program
– Decision Support Framework (DSF)
– RVF Modelling
– RVF Risk Factors
– Economic Scenario Analysis of DSF
• Risk-Based Decision Support Framework
– 2006-2007 Impact Study
– Process and Publication
– Future Directions
2 Overview
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Risk-Based Decision Support Framework (DSF)
• Participatory Process: – Risk map
– Matrix of actions matched to events • RVF epizootic events list
• Action categories
• Stakeholder built
– Selected information, resources and references
3
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RVF Modellng
• A spatial, agent based, stochastic model
• Mechanisms of RVF persistence
• Predict risk, impact of RVF and interventions
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• Descriptive analyses
• Regression models:
–Generalized Linear Mixed models
Poisson model for incidence
Logit models for prevalence
–MCMC/spatial multiple membership model
To account for spatial autocorrelation
Risk Factor Analysis
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Variable Source Description
Livelihood zones FEWSNET Livelihood practices as at 2006
Land cover FAO on-line
database
Global land cover data, 2000
Precipitation ECMWF Monthly minimum, maximum and
average for the period: 1979 - 2010
NDVI Spot Vegetation Monthly average, minimum,
maximum values from: 1999 - 2010
Human population Kenya National
Bureau of
Statistics
Human and household census for
1960, 1970, 1980, 1990, 1999
Elevation CSI SRTM
Soil types FAO FAO’s Harmonized World Soil
Database (HWSD), 2009
Wetlands (area as % of
total)
ILRI GIS Unit
Parks/reserves (area as
%)
ILRI GIS Unit
Risk Factor Analysis - predictors
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Divisions that have had RVF
outbreaks
in Kenya between 1912 and 2010
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0%
1%
2%
3%
4%
5%
6%
7%
8%
Month
Pro
po
rtio
n o
f d
ivis
ion
s af
fect
ed
14%
16%
18%
505 divisions -1999 population census
20.2 % (n = 102) of the divisions have had an outbreak at least once
Mean outbreak interval : 5.4 (4.4 – 6.4) years
Temporal distribution of RVF outbreaks:
1979 - 2010
Risk Factors
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Variable Level β SE β SE
Fixed effects
Constant -3.74 0.69 -6.18 0.92
Precipitation 0.11 0.03 0.16 0.04
NDVI 2.68 0.80 3.29 0.83
Soil types Solonetz 1.34 0.49 1.64 0.62
Luvisols 1.24 0.45 1.80 0.59
Elevation < 2300 m 0.00 - 0.00 -
> 2300 m -2.99 0.64 -3.79 0.95
Random effects
Livelihood zones 3.16 0.61 9.37 3.02
Deviance 841.57
Models for the persistence of outbreaks
Multi-level Poisson model MCMC/Bayesian model
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Sandik Case Definition:
RVF Compatible Event
• Abortion
• Heavy rains and mosquitoes
• Froth from the nose, often with epistaxis
• Salivation
• Fever
• Death, particularly in young animals
An outbreak in sheep and goats involving abortions during periods of heavy rain and abundance of mosquitoes, with two or more other
listed clinical symptoms being observed in the herd, should be reported as RVF compatible disease to public health authorities.
Cattle in the same area will be affected with similar but less severe symptoms, and rarely camels.
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Average Timeline
Average time from:
•Onset of rains to mosquito swarm: 33.1 days
•Mosquito swarm to first animal case: 19.2 days
•First animal case to first human case: 21 days
•First humane case to medical service intervention: 35.6 days
•First medical service intervention to first veterinary intervention: 12.3 days
•First animal case to veterinary service intervention: 68.9 days
Rains Vectors Livestock Human Human Vet
33.1 days 19.2 days 21 days 35.6 days 12.3 days
Risk Factors Cases Response
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So why was the
response so late?
• All or nothing decision
• Waiting for perfect information
• Risk avoidance 11
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Optimal Decision-Making
• Recognizes – The need to balance the need information
against the need for a timely response
– That information will be imperfect
– That decision making involves taking risk
• How can we make decision-making less risky – Phased
– Shared
12 Lessons
Learnt
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Decision Points
• Early warning or alerts
• Localized heavy rains observed
• Localized flooding reported
• Mosquito swarms
• Livestock disease
• Laboratory confirmation
• Human disease
• Laboratory confirmation
13
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Progressive Risk Mitigation
• Consequence x
probability of outcome
• Probability increases at
each decision point
• Justification for
investment in risk
mitigation increases
• Risk of making the
wrong decision
decreases
Phased Decision-Making
0
0.2
0.4
0.6
0.8
1
1.2
0 1 2 3 4 5
Decision Points
Pro
ba
blity
of
Ou
tco
me
14
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Decision-Making Trade Off
15
No Info Perfect Info
Risk of Being to Late
Risk of Being Wrong
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Methods
• Initial workshop – RVF events
sequenced
– Interventions inventoried
– Actions matched to event sequences
• Expert review
• Follow-up workshop
• Peer review
16 Methods
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Tool vs Framework • Original name caused confusion
– Informative dialogue
• Modellers assumed it was model
– Efforts to ‘fix’ the tool
– The tool itself should output the decision
• Strength of the ‘framework’
– Created and owned by decision-makers
– Models can inform the discussion, but
not drive the process
17
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The Future
DSF managing risk in trade
• Transparent framework for managing
RVF
• Market events and interventions
• Regional meeting in Dubai
– Horn of Africa, Middle East, OIE
– regional framework for trade
– extend to other disease.
– Current Application
• Kenya and Tanzania
• Development partnerships?
18