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GIS analysis of East Coast fever risk in small holder dairy production systems in Kenya
| Acronym: | ECFRisk |
| Project type: | Consulting Series RTD |
| Time frame: | 2003 - 2004 |
| Funding agency: | IFAD |
| Geographic keyword: Kenya | Africa | |
| General keyword: Disease modeling | Remote sensing | |
| Specific keyword: Ripicephalus appendiculatus | Tick | Risk assessment | Stepwise multivariate regression analysis | East coast fever |
East Coast Fever (ECF) is an endemic cattle disease constraining livestock production in smallholder, agro-pastoral and pastoral systems in Kenya. This disease is caused by the protozoan parasite Theileria parva and is transmitted by the brown ear tick Rhipicephalus appendiculatus. In the efforts to combat the losses resulting from this disease, several measures have been introduced, one of which is cattle immunisation through the "Infection and Treatment Method".
The success of ECF immunisation depends on the availability of information on the distribution of the disease and the different levels of the disease risk, as this aids prioritisation of interventions. However, the information on the disease risk is only available for few areas where detailed epidemiological studies have been conducted. As epidemiological studies are expensive and time-consuming, it is hard to conduct these studies for the whole country. Risk mapping using Geographical Information Systems (GIS) provides an opportunity to use existing data sets to predict the risk levels over a wider area.
The objective of this study was to collate and exploit existing data sets from various ILRI studies in order to develop further GIS analysis of ECF risk in the various production systems in Kenya ranging from intensive smallholder to extensive pastoral systems. Obtained results contribute to decision support and planning of ECF immunisation.
Based on the data availability a bi-focal approach was designed:
Area-wide vector mapping for Kenya with special emphasis on the discrimination between areas of permanent tick presence (endemic), marginal areas where the tick only occurs in favourable seasons of favourable years (high epidemic risk) and areas of absence of ticks.
Restricted serology mapping within areas representative (i.e. eco-climatically matching) of existing training data sets as a surrogate for disease mapping.
For the area-wide vector mapping both a negative and a positive data set was compiled to feed a stepwise multivariate regression model. The independent variables were derived from a one-year time series of low-resolution satellite imagery using Fourier transformation, enabling the extraction of seasonality patterns. The resulting 1km resolution maps were of higher quality than the maps previously created, especially in marginal areas and at the cold limit of the tick distribution. Results were validated using a separate validation data set.
In the next step serology data were used to determine the disease risk. Since the serology data set is not exhaustive and only available for a limited number of ecological zones, not representative for the entire distribution area of R. appendiculatus in Kenya, this risk map was limited to the areas where the different ILRI studies were conducted.
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