Weather index insurance for agriculture wb wii_paper_nov_2011
Index-based insurance for AAT control
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Designing an index-based insurance to control African Animal Trypanosomosis in sub-humid zone of West Africa PhD candidate, Ahmadou H. Dicko (UCAD, WASCAL) PhD, Jérémy Bouyer (CIRAD) PhD, Marc Müller (FAO) PhD, William M. Fonta (WASCAL)
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Transcript of Index-based insurance for AAT control
Designing an index-based insurance to control African Animal
Trypanosomosis in sub-humid zone of West Africa
PhD candidate, Ahmadou H. Dicko (UCAD, WASCAL)
PhD, Jérémy Bouyer (CIRAD)
PhD, Marc Müller (FAO)
PhD, William M. Fonta (WASCAL)
Background
• The demand for livestock products will double in the 2050horizon (Herrero et al. 2009)
• Most of the livestock products are made by smallholdersin developing countries
• These households are vulnerable to socio-economic andclimatic shocks
• They face many challenges on their daily activities:• Tsetse transmitted trypanosomosis is one of the most
important
(Thornton et al. 2007)
Tsetse fly and Africa development
African Animal Trypanosomosis burden
• African animal Trypanosomiasis (AAT) is a fatalvector-borne disease that causes serious economic lossesin livestock.
• Most endemic countries are among the poorest in thecontinent
• Yearly cost of African trypanosomosis is more than 1bnUS$
(Alsan et al. 2013)
Index-insurance as a solution ?
Index-based insurance in a nutshell
• Based on a index (e.g NDVI) correlated to the losses• Payout are made on a agreed-upon threshold of the index• Advantages : Low transaction cost and no moral hazardand adverse selection
• Disadvantages : Imperfect correlation between chosenindex and losses ( Basis risk )
• Basis risk have to be as low as possible in an optimalinsurance design
Index-Based Livestock Index Insurance
.....
Index-based livestock index insurance (IBLI)
.
Impact
. Monitor
Index-based animal disease insurance
.....
Index-based animal disease insurance (IBADI)
.
Impact
. Monitor
The key question is how to monitor thelevel of trypanosomosis ?
Input: Entomological data
10°N
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12°N
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14°N
5°W 4°W 3°W 2°W 1°W
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Input: Bovine trypanosomosis data
• Data from 3 surveys(two from PATTEC)
• Data on:• Serological status• Parasitological
status• Level of anemia• Control variables
(age, cattle breed)10°N
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Sero. prevalence (%)●
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[0,20](20,40](40,60](60,80](80,100]
Covariates: remote sensing data
• 10 years of monthlytime series of remotesensing data
• MODIS product(LST, NDVI, MIR)
• Rainfall estimatefrom FAO (RFE2)
RFE
0 20 40 60 80 100
NDVI
−0.2 0.0 0.2 0.4 0.6 0.8
MIR
0.0 0.1 0.2 0.3 0.4 0.5 0.6
DLST
20 25 30 35 40 45 50
NLST
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DEM
100200300400500600700800
Material and methods
..Trypanosomosis risk ∼ EIR.
Tsetse apparent density
.
Tsetse habitat suitability
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Tsetse infection rates
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Regularized logistic regression
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Count regression
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Logistic regression
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Climate ∼ Remote sensing data
Material and methods
The chosen index is known by entomological inoculation rate(EIR) or tsetse challenge. It is the mathematical product of:
• Tsetse apparent density• Number of tsetse flies caught per day per trap
• Tsetse infection rates• Trypanosome infection rates on tsetse
This modeling framework extends the tsetse habitat suitabilityindex developed in previous studies (Dicko et al. 2014).
Predicted risk index (tsetse challenge)
dry2005 rainy2005
0 5 10 15 20 25 30 35 40
Figure: Prediction tsetse challenge for dry and rainysaison 2010
Index validation against bovinetrypanosomosis data
The relation between EIR and following metrics will beinvestigated:
• Serological test results (antibodies for trypanosome)• Level of parasitaemia (buffy coat techniques)• Illness status (seropositive and anemic)
Index validation against bovinetrypanosomosis data
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Figure: Prediction on testing data (not used to train the models)
