A contribution towards simplifying area-wide tsetse surveys using medium resolution meteorological satellite data

A raster or grid-based Geographic Information System with data on tsetse, trypanosomiasis, animal production, agriculture and land use has recently been developed in Togo. The area-wide sampling of tsetse fly, aided by satellite imagery, is the subject of two separate papers. This paper follows on a first paper, published in this journal, describing the generation of digital tsetse distribution and abundance maps and how these accord with the local climatic and agro-ecological setting. Such maps when combined with data on the disease, the hosts and their owners, should contribute to the knowledge of the spatial epidemiology of trypanosomiasis and assist planning of integrated control operations. Here we address the problem of generating tsetse distribution and abundance maps from remotely sensed data, using a restricted amount of field data. Different discriminant analysis models have been applied using contemporary tsetse data and remotely sensed, low resolution data acquired from the National Oceanographic and Atmospheric Administration (NOAA) and Meteosat platforms. The results confirm the potential of satellite data application and multivariate analysis for the prediction of the tsetse distribution and abundance. This opens up new avenues because satellite predictions and field data may be combined to strengthen and/or substitute one another. The analysis shows how the strategic incorporation of satellite imagery may minimize field collection of data. Field surveys may be modified and conducted in two stages, first concentrating on the expected fly distribution limits and thereafter on fly abundance. The study also shows that when applying satellite data, care should be taken in selecting the optimal number of predictor variables because this number varies with the amount of training data for predicting abundance and on the homogeneity of the distribution limits for predicting fly presence. Finally, it is suggested that in addition to the use of contemporary training data and predictor variables, training and predicted data sets should refer to the same eco-geographic zone.

The spatial pattern of trypanosomosis prevalence predicted with the aid of satellite imagery

Information on the spatial pattern of African animal trypanosomosis forms a prerequisite for rational disease management, but few data exist for any country in the continent. The present study describes a raster or grid-based Geographic Information System for Togo, a country representative of subhumid West Africa, with data layers on tsetse, trypanosomosis, animal production, agriculture and land use. The paper shows how trypanosomosis prevalence and packed cell volume (PCV) map displays may be predicted from correlations between representative field data and environmental and satellite data acquired from the National Oceanographic and Atmospheric Administration (NOAA) and Meteosat platforms. Discriminant analytical methods were used to assess the relationship between the amount of field data used and the accuracy of the predictions obtained. The accuracy of satellite derived predictions decreases from tsetse abundance to trypanosomosis prevalence to PCV value. The predictions improve when eco-climatic and epidemiological predictors are combined. In Togo, and probably elsewhere, the patterns of trypanosomosis prevalence and PCV are much influenced by animal husbandry and other anthropogenic factors. Additional predictor variables, incorporating these influences might therefore further improve the models.

The area-wide epidemiology of bovine trypanosomosis and its impact on mixed farming in subhumid West Africa; a case study in Togo

This paper reports on an area wide study of all major variables determining the expression of trypanosomosis in cattle in the subhumid eco-zone of West Africa, taking Togo as an example. To enable systematic area-wide sampling, the country was divided in 311 grid-squares of 0.125 x 0.125 sides. Cross-sectional surveys were then conducted to generate maps or digital layers on cattle density, herd structure, ownership and breed. These data layers, except for the breed data, were subjected to a cluster analysis in order to define spatial patterns in animal husbandry systems. This analysis revealed two main systems: one is oriented towards integration with crop-agriculture and a second towards investment in cattle. These two systems could be further characterised by incorporating breed data. Zebu cattle and their crossbreeds are more favoured in the second system. The breed distribution map shows the actual situation but also serves to predict the outcome of progressive crossbreeding. An area wide trypanosomosis survey allowed the production of prevalence maps for Trypanosoma congolense, T. vivax and the associated packed cell volume (PCV) values. A simple curvi-linear relationship was established between vector density and disease prevalence. The regression between disease prevalence and PCV for taurine and zebu plus crossbreeds separately, revealed that taurine cattle maintain a comparatively high PCV level particularly in high prevalence scenarios. The relationship between the average herd PCV and cattle density suggests that herd PCV value may provide a mirror for the number of animals not kept because of the prevailing risk. The regression between agricultural intensity and cattle density subsequently in areas with decreasing herd PCV values reveals that the level of integration of cattle in crop production decreases with a decreasing PCV. Thus, despite the presence of taurine animals in Togo, the omnipresence of tsetse in particular Glossina tachinoides, remains a major obstacle to cattle raising and indirectly mixed farming development and intensification. It is argued that only with the present type of wide scale, spatial studies it becomes possible to clarify all the major variables influencing the expression of trypanosomosis. Spatial epidemiological studies at a macro level may form the basis for area wide trypanosomosis control in West Africa.

Can remotely sensed meteorological data significantly contribute to reduce costs of tsetse surveys?

A 0.125 degree raster or grid-based Geographic Information System with data on tsetse, trypanosomiasis animal production, agriculturerkina> and land use has recently been developed in Togo. This paper addresses the problem of generating tsetse distribution and abundance maps from remotely sensed data, using a restricted amount of field data. A discriminant analysis model is tested using contemporary tsetse data and remotely sensed, low resolution data acquired from the National Oceanographic and Atmospheric Administration and Meteosat platforms. A split sample technique is adopted where a randomly selected part of the field measured data (training set) serves to predict the other part (predicted set). The obtained results are then compared with field measured data per corresponding grid-square. Depending on the size of the training set the percentage of concording predictions varies from 80 to 95 for distribution figures and from 63 to 74 for abundance. These results confirm the potential of satellite data application and multivariate analysis for the prediction, not only of the tsetse distribution, but more importantly of their abundance. This opens up new avenues because satellite predictions and field data may be combined to strengthen or substitute one another and thus reduce costs of field surveys.