Where should we target Infection and Treatment Method (ITM) distribution? A GIS based approach applied to Kenya, Malawi, Tanzania and Uganda
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Where should we target Infection and Treatment Method (ITM) distribution? A GIS based approach applied to Kenya, Malawi, Tanzania and Uganda

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Presented by P. Ochungo, I. Baltenweck, H. Kiara, J. Poole and W. Theuri, January 2012

Presented by P. Ochungo, I. Baltenweck, H. Kiara, J. Poole and W. Theuri, January 2012

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    Where should we target Infection and Treatment Method (ITM) distribution? A GIS based approach applied to Kenya, Malawi, Tanzania and Uganda Where should we target Infection and Treatment Method (ITM) distribution? A GIS based approach applied to Kenya, Malawi, Tanzania and Uganda Presentation Transcript

    • Where should we target Infection and Treatment Method (ITM) distribution? A GIS based approach applied to Kenya, Malawi, Tanzania and Uganda Jan 2012 Prepared by P. Ochungo, I. Baltenweck, H. Kiara, J. Poole and W. Theuri (ILRI)
    • Background on East Coast Fever  East Coast Fever (ECF), also known as Theileriosis, is a devastating tick-borne disease in East, Central and Southern Africa, that puts 28 million cattle at risk of mortality and causes production losses to the tune of $121million per year (Mcleod &Randolph, 2000). It is caused by Theileria parva parasites and transmitted by the vector Rhipicephalus appendiculatus (brown ear) ticks.  It is the major cattle disease in the smallholder dairy and pastoral production systems in East Africa and is responsible for about 50% calf morbidity and 75% mortality (IFAD,2007).  Mortality rates due to ECF can be as high as 80 – 100% when introduced to susceptible cattle. References: McLeod, R. and Randolph, T.(2000). Product Development Plan: East Coast Fever Vaccine for Africa. Unpublished Report to the International Livestock Research Institute, Nairobi, Kenya, 44 pp. IFAD, (2007). Programme for Enhancing the Impact of Immunization Against East Coast Fever with an Improved Sub Unit Vaccine on the Smallholder Dairy Sector in Eastern Africa [online]. Available at http://www.ifad.org/lrkm/tags/376.htm. [Accessed January 2012] Map showing distribution of ECF in Africa
    • Preventive methods against ECF ProsMethod Cons Acaricides – most common preventive measure employed in E Africa.  Easy to access  Relatively cheap per treatment Resistance of ticks to acaricides. Higher cost of newer acaricides Environmental pollutants that may also contaminate milk and meat. Rearing of tick resistant breeds Controlled grazing – rotational and zero grazing ITM vaccination – Infection and Treatment method. A vaccine technology that was developed over 30 years ago. Involves innoculating live Theileria parva parasites into the animal while simultaneously treating with a long acting antibiotic. Requires only one vaccination in a lifetime. Works effectively if used under proper supervision. Delivery of the technology is complex, requiring a ‘cold chain’ infrastructure.  Relatively expensive. Sources: Minjaw, B & Mcleod, A. (2003) Tick Borne Diseases and Poverty. Natural resistance to TBDs  Herd management problems can develop.  Labour and knowledge intensive  Not applicable everywhere. Animals are not exposed to TBDs IFAD, (2007). Programme for Enhancing the Impact of Immunization Against East Coast Fever with an Improved Sub Unit Vaccine on the Smallholder Dairy Sector in Eastern Africa [online]. Available at http://www.ifad.org/lrkm/tags/376.htm. [Accessed January 2012]  Disease resistant breeds often have less productive traits so are less preferred.  Long periods required to breed in sufficient numbers.
    • A vet or Community Animal Health Worker Vaccinating Tanzania Maasai animals against ECF
    • Objectives of the Study To estimate spatially the number of cattle that can be potentially vaccinated against ECF using ITM, and where these cattle are. Current estimates are based on broad figures of cattle population (irrespective of breed, ECF risk and farming system). This might guide GalvMed and commercial players in identifying market demand for the vaccine.
    • Hypotheses ECF being the single most important cattle disease, farmers are likely to invest in order to prevent it. Exotic cattle (pure or cross bred) are higher value and their owners are more likely to invest in prevention.
    • Methodology Cattle population map Farming systems map Breed differentiated map (local/ exotic cattle) Step 1: Apply survey data or expert opinion data to farming systems map to get proportions of breeds in herd Step 2: Apply these proportions into cattle population map to derive numbers of local/exotic cattle Step 1: Deriving a breed differentiated map
    • Methodology Step 2: Calculating number of cattle needing vaccination by ITM We use the following formula to calculate number of cattle needing ITM vaccination: CattleITM= (ExoticCPop x 100% + IndigenousCPop x 30%) x ECF% Where: CattleITM is defined as the estimated number of heads of cattle to be ITM vaccinated per kmsq in a specific location. ExoticCpop = No. of exotic cattle in the herd IndigenousCpop = No of indigenous cattle in the herd ECF% = ECF risk% Note: the weights on exotic and indigenous cattle were set at 100% and 30% respectively, representing the estimated mortality rates due to ECF for these genotypes. Assumption: farmers facing a x% mortality rate will vaccinate x% of their herd. Countries: Kenya, Malawi, Tanzania and Uganda
    • Deriving cattle breeds using farming system maps – Kenya and Uganda Countr y Farming system (S&S) Local cattle (%) Crossbred cattle (%) Kenya LG – arid/ semi-arid 100 0 LG – humid/subhumid 100 0 LG- hyperarid 100 0 LG- temperate 100 0 MR- arid/ semi-arid 86.3 13.7 MR- humid/ subhumid 76.55 23.45 MR- temperate 38.05 61.95 Countr y Farming system (S&S) Local cattle (%) Crossbred cattle (%) Uganda LG- arid/ semi-arid 100 0 LG-humid/ subhumid 100 0 LG- hyperarid 100 0 LG-temperate 100 0 MR- arid/ semi-arid 86.3 13.7 MR- humid/subhumid 76.55 23.45 MR- temperate 38.05 61.95 Data source for MR systems: EADD Baseline survey, 2008- EADD sites are located only in MR systems. Data source for LG systems: Expert opinion. Farming systems: Global Sere and Steinfield, Version 4, 2000. LG- Livestock only systems MR- Mixed rainfed systems
    • Deriving cattle breeds using farming system maps – Tanzania and Malawi Country Farming system (S&S) Local cattle (%) Crossbred cattle (%) Tanzania LG- arid/ semiarid 92.525 7.475 LG – humid/ subhumid 98.2 1.8 MR- arid/ semiarid 97.92857143 2.07142857 MR-arid/semiarid-LGP60 41.95 58.05 MR-humid/subhumid LG60 98.16667 1.83333 Countr y Farming system (S&S) Local cattle (%) Crossbred cattle (%) Malawi LG- arid/ semiarid 100 0 LG-humid/subhumid 100 0 LG- temperate 100 0 MR-arid/ semiarid 99.9969 0.003067 Data source: Tanzania bureau of statistics, 2008 Data source: Chiwayula et.al., 2010 (FAO Repository documents)LG- Livestock only systems MR- Mixed rainfed systems
    • Derived breed maps – Local cattle density Cattle density Farming systems with % local cattle Local cattle density Sources- Cattle density - Gridded Livestock Population of The World (FAO, 2007); Farming Systems: Global Sere and Steinfield Version 4, 2000.
    • Derived breed maps – Exotic cattle density Cattle density Farming systems map with % exotic cattle exotic cattle density Sources- Cattle density - Gridded Livestock Population of The World (FAO, 2007); Farming Systems: Sere and Steinfield V4
    • ECF Risk Map Values in the map represent probability of disease occurrence ECF Risk map: • Based on predicted values of habitat suitability for Rhipicephalus appendiculatus. • Predictions based on a logistic regression of reported presence/ absence of tick species against 49 remotely sensed & interpolated environmental variables. Pros of this map • presents a good estimate of areas at risk from ECF disease Cons of this map • based on presence/ absence of tick whereas abundance of vectors should also be considered. • probability of ECF risk is poorly correlated with farmers perception of disease risk. Source: Minjaw, B & Mcleod, A. (2003) Tick Borne Diseases and Poverty. The impact of ticks and tiick borne diseases on the livelihood of small scale and marginal livestock owners in India and eastern and southern Africa. Research report, DFID Animal Health Programme, Centre for Tropical Veteri- nary Medicine, University of Edinburgh, UK.
    • Approach for generating the map showing potential number of cattle needing ITM vaccination Local cattle density 30% Breed maps were weighted according to the formula and then multiplied by ECF risk map Exotic cattle density 100% ECF Risk CattleITM Numbers of cattle requiring ITM vaccination were then calculated from this map, at country level and then at province/ region level.
    • CattleITM map (Total numbers of animals): Potential density of cattle to be vaccinated using ITM The resulting map shows numbers of cattle that can be potentially vaccinated against ITM 154,976 4,884,543 1,836,925 4,519,803
    • Summaries of numbers of cattle to vaccinated by administrative region/province Kenya Uganda Malawi Tanzania Source: CattleITM map
    • Conclusions and way forward ■ The largest demand is in Tanzania (42%) with 2 main zones (Central and the Lake region) although the demand is relatively dispersed, making set up of distribution networks more complex ■ The demand is more concentrated in Kenya (Central and Western regions) and Uganda (Eastern and Western regions) where ECF risk is relatively high and exotic cattle are kept ■ Given the paucity of spatially distributed data, these results are based on assumptions that can be modified if more reliable data become available. ■ We didn’t take into account feeding system (open grazing versus stall feeding where risk of ECF may be lower due to lower exposure to ticks) due to data unavailability for all the systems and countries under consideration. ■ Despite these limitations, these results can guide scale and targeting of distribution networks of the ITM vaccine in East Africa
    • Contacts For More Information, please contact: Pamela Ochungo, pam.ochungo@cgiar.org Isabelle Baltenweck, i.baltenweck@cgiar.org