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Recent advances in the use of geographical information systems with particular relevance to veterinary parasitology.

Recent advances in the use of geographical information systems with particular relevance to veterinary parasitology. Guy Hendrickx (1), Jan Biesemans (1) and Reginald De Deken (2). (1) Avia-GIS, Risschotlei
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Recent advances in the use of geographical information systems with particular relevance to veterinary parasitology. Guy Hendrickx (1), Jan Biesemans (1) and Reginald De Deken (2). (1) Avia-GIS, Risschotlei 33, 2980 Zoersel, Belgium (corresponding authors). (2) Institute for Tropical Medicine, Nationalestraat 101, Antwerp, Belgium. Extended version from: Hendrickx, G., Biesemans J. and De Deken R. (2004) The use of GIS in Veterinary Parasitology. In : GIS and Spatial Analysis in Veterinary Science, Durr, P.A and Gatrell A.C. Editors. CABI Publishing, Wallingford, UK, Abstract During the past decades the publication of papers interest related to the use of geographical information systems (GIS) and/or remote sensing (RS) and of particular interest to veterinary parasitology veterinary followed an exponential trend. The use of GIS and RS is now generally accepted by the scientific community as a major tool contributing to the understanding of epidemiological processes sensu lato: disease vector host environment. Nevertheless, whilst most people now are aware of the potential of these techniques, many still hesitate to use these tools for research or decision making. This paper reviews recent advances towards more widespread routine use of GIS and RS in parasitology. After a brief introduction setting historical trends and milestones, followed by a more detailed review of past work in three chosen fields, tsetse transmitted trypanosomosis, liver fluke and East Coast fever, the authors discuss how Geographical Information Systems-Science (GIS) is currently evolving towards STIS, Space-Time Information Systems-Science, an even more holistic multidisciplinary approach encapsulating not only space but also the time domain. An inventory is made of the different topics addressed during the last two years separately for disease mapping, spatial epidemiology and decision support systems. Current trends show that systems based on spatial data analysis and the use of remote sensing are now applied to a wide variety of diseases and geographical areas suggesting that GIS/RS are now widely used for research and decision making purposes. Most of the tools and ingredients are now available to further promote the emergence of STIS reasoning in veterinary parasitology, provided scientists from different disciplines are prepared to share data and experience. More than ever such technologies and collaborative networks are needed to help understand and cope with new challenges of a changing world : climate change, globalization and emerging diseases. 2. An historical perspective During the past decades the publication of papers of veterinary and human health interest related to the use of geographical information systems (GIS) and/or remote sensing (RS) followed an exponential trend (Fig.1 - curve). Some events have marked the displayed curve. Prior to the review published by Hugh-Jones (1989) in Parasitology Today on the applications of remote sensing to the identification of habitats of parasites and disease vectors, only a few papershave been recorded. Of these, 1/3 were related to parasitology and mainly aimed at identifying mosquito habitats (malaria and Rift Valley fever). A second major event was the publication in 1991 by Preventive Veterinary Medicine of an issue on Applications of remote sensing to epidemiology and parasitology. This clearly raised interest in these new technologies and the average number of publications increased from three papers per two years to 17 per year in the first half of the 1990 s. In the second half of the 1990 s numbers further raised exponentialy and curently more than 60 papers are recorded per year, 60% of which are related to parasitology and vector born diseases. A further breakdown per subject is given in figure 1 (pie-chart). Papers on four major disease vectors predominate (69% of published papers): (i) mosquitoes (29%) with topics including malaria, Rift Valley fever, Lacrosse encephalitis, Dengue, West Nile and Eastern equine encephalitis, (ii) tsetse (16%) and (mainly) animal trypanosomosis, (iii) ticks (13%) as vectors of Lyme disease and tick borne encephalitis in Europe and Northern America as well as some African tick born diseases, and (iv) snail intermediary hosts (11%) of schistosomes and liver fluke. Currently culicoides midges, major players on the arboviroses and emerging diseases scene, are an upcoming subject. The applications of GIS and RS in epidemiology and parasitology have been reviewed by several authors (44 recorded papers). The most recent in dept summary of one decade of research was a special volume of Advances in Parasitology published by Hay and colleagues (2000) in which all relevant topics were reviewed in great detail providing the scientific community with the latest landmark in this field. The use of GIS and RS is now generally accepted by the scientific community as a major tool contributing to the understanding of epidemiological processes sensu lato: disease vector host environment. Nevertheless, whilst most people now are aware of the potential of these techniques, many still hesitate to use these tools for research or decision making. This paper reviews recent advances towards more widespread routine use of GIS/RS and Space Time Information Systems (STIS) in parasitology. First we will review past trends: How has GIS applied to the spatial epidemiology of parasitic diseases evolved from hard copy mapping exercises to interactive decision support tools? To do this we will focus on three case studies: (1) an insect born disease: tsetse transmitted animal trypanosomosis with particular reference to West Africa, (2) an intermediary host disease, Fasciola hepatica in the Southern US and East Africa, and (3) a tick borne disease, East Coast Fever in East and Southern Africa. In the second part current and future trends will be discussed, starting with a discussion about the implications of using GIS at an operational level and the need to fully integrate all aspects of time and space to achieve this goal. In this part a review of literature published since 2000 (the post Hay et al era) on topics relevant to GIS and parasitology is given. 3. GIS and the spatial epidemiology of parasitic diseases From hard-copy maps to multidisciplinary information systems 3.1 Tsetse transmitted trypanosomosis Arguably the area-wide knowledge of the different factors affecting the interactions between vectors, parasites and hosts is a prerequisit to understand the spatial epidemiology of the disease and a strong basis for rational trypanosomosis management. Thus a first step towards understanding those interactions at a macro-scale will include the systematic mapping of : the distribution and abundance of the different tsetse species (vectors); the occurrence (prevalence) and expression (anaemia) of trypanosomosis (parasites); the distribution and relative importance of cattle breeds and cattle management systems (hosts) Area-wide mapping Vectors. Since the early workers established the link between Nagana, caused by trypanosomes, and the tsetse vector one century ago (cf. Bruce and Kleine reviewed by Hoare 1972), considerable efforts have been made to map the distribution of the different tsetse species. This wealth of information gathered by often anonymous field workers at country level has regularly been compiled to produce distribution maps at a sub-regional or continental scale (Newstead 1924, Nash 1948, Potts 1953, Machado 1954, Buxton 1955, Maillot 1957, Rickenbach 1961, Ford 1963, Ford and Katondo 1973). The maps produced by Ford and Katondo (1973) are still considered to be an international standard. They include nine sheets of 1:5,000,000 maps describing the distribution of the different tsetse species per group (palpalis, morsitans, fusca) and per sub-region (Western, Eastern and Southern Africa). They have been locally updated by several authors (FAO 1980, Katondo 1984, Moloo 1985, Gouteux 1990). A detailed review of past and present tsetse distributions in Southern Africa is given by Van den Bossche and Vale, (2000) for Malawi, Mozambique, Eastern Zambia and Zimbabwe. Whilst historical tsetse distribution patterns are often well documented at a country scale (e.g. for West Africa Challier et Laveissière 1977 and 1981), the problem of mapping tsetse abundance has less frequently been addressed. Most efforts towards that goal are limited to the monitoring of tsetse populations in areas earmarked for vector eradication before, during and after suppression campaigns, e.g. the pastoral zone of Sidéradougou (3.000 km²) - Burkina Faso (Cuisance et al. 1984b). In Northern Côte d Ivoire ( km²) tsetse surveys carried out from 1978 to 1981 to help define a rational control strategy for the whole area, yielded detailed tsetse distribution and abundance maps of all species present (Clair 1982, Clair and Lamarque 1984). In the Gambia ( km²) an abundance map of G. m. submorsitans was produced (Rawlings et al. 1993). More recently in Togo ( km²) a set of national distribution maps at a degree grid resolution for all species present, G. m. submorsitans, G. longipalpis, G. tachinoides, G. palpalis palpalis, G. fusca and G. medicorum and abundance maps for both riverine species, G. tachinoides and G. p. palpalis, was produced in the 1990 s (Hendrickx et al. 1999a). Parasites. Whilst tsetse survey results are well documented, few records are known of systematic mapping of trypanosome distribution and prevalence rates. Most studies report results in a tabular form per administrative unit (e.g. Awan et al. 1988, Agu et al. 1989). Other examples include some spatial aspects such as reported by Corten et al. (1988) in Southwest Zambia where surveys revealed that the extent of the trypanosomosis problem covered a wider area than expected from historical fly distribution data alone. The recorded fly abundance was expected to reflect disease risk (Cuisance et al. 1984a, Clair and Lamarque 1984). Therefore trypanosomosis surveys were often not conducted. In parts of the Northern Côte d Ivoire area, Camus (1981a) conducted prevalence surveys in 191 herds of the 1,200 herds monitored by the Société pour le Développement de la Production Animale. Sixteen (16) cattle were sampled per herd. Herds were classified as either positive or negative. Results were summarised in a table and some spatial variation of disease prevalence was shown. No link was made with tsetse maps. An analysis of contemporary zootechnical data showed a significant difference between positive and negative herds. In the Gambia example (Rawlings et al. 1993), a series of integrated trypanosomosis control measures were proposed, adapted to the different levels of G. m. submorsitans abundance. In a later study Snow et al. (1997) showed positive correlations between the recorded tsetse abundance figures and disease prevalence in cattle, small ruminants and equids. Only a few studies aimed at area-wide trypanosomosis mapping. In Togo in addition to the entomological surveys mentioned above, herds were systematically sampled at the same spatial resolution (0.125 degree grids). After transformation, obtained results yielded detailed country-wide raster maps of parasite distributions and prevalence as well as herd aneamia (Hendrickx et al. 1999b). This work was later extended to Western Burkina Faso along the Mouhoun river system. Data one disease prevalence and the prevalence of anaemic cattle were combined to map epidemiological patterns clearly showing changing risk levels according to the impottance of drainage systems (Hendrickx and Tamboura, 2000). In Southern Africa point measurement maps were produced summarising trypanosomosis surveys conducted in the 1990 s in Malawi (159 sampling sites), Mozambique (274 sampling sites), Zambia (128 sampling sites) and Zimbabwe (62 sampling sites) (Van den Bossche and Vale, 2000). Hosts. Epstein (1971) discussed the distribution of cattle breeds on a continental scale. In Western and Central Africa ILCA (1979) and FAO (1987) produced cattle breed maps per country. Per country figures are given for each larger administrative region. Maps combined with pie-charts depict the presence of dominant cattle breeds. In addition information is provided on breed performance and husbandry systems. No maps are given of the latter. In Northern Côte d Ivoire Camus et al. (1981) studied, as part of the same study on trypanosomosis prevalence mentioned above, breed distributions and the effect of increasing zebu pressure on sedentary taurine herds after the droughts of the 1970 s. Catle were classified as either Baoulé (West African Shorthorn Taurine), N dama (West African Longhorn Taurine), Zebu or Taurine Zebu crosses. Data were gathered from the SODEPRA extension workers. Schematic maps are given of sedentary cattle distributions per breed separately for reproductive females and males. Densities are shown as dots of respectively 500 or 5,000 head. The economic impact of trypanosomosis on those different breeds was further discussed by Camus (1981b). In the Gambia the ITC (International Trypanotolerance Centre) team involved in the examples given above developed a low cost rapid appraisal methodology where results of field surveys are combined with two socio-economic questionnaires including topics on (i.) farming systems and village economics and (ii.) livestock and tsetse (Snow et al. 1993). Finally during the same Togo-study mentioned above an exhaustive country wide cattle survey yielded degree cattle distribution and breed maps (Hendrickx et al. 1999b). Cattle breeds were characterised as either trypanosusceptible, i.e. Zebu, trypanotolerant, i.e. West African Short Horn Somba or crossbreeds (Fig.2 Maps A-B-C-D). Obtained results using a phenotypic key were validated using micro-satellite technology (a measurement of Zebu introgression) on a sub sample (Hendrickx et al and Dao 1998) Remote sensing to assist disease mapping The influence of climatic variables on the distribution and abundance of tsetse has long been recognised, both at a local (Nash 1937) and regional level (Nash, 1948) through years of field study. Nowadays, the increased availability of satellite imagery allows us to draw up much improved vector distribution maps (Cline 1970, Hugh-Jones 1989, Riley 1989, Epstein et al. 1993, Washino and Wood 1994, Hay et al. 1997). Satellite images offer several advantages over field surveys; data are free from any human bias, make remote places accessible, are continuously produced and show real time information. Rogers and Randolph (1993) pioneerd the application NOAA derived NDVI (Normalised Difference Vegetation Indices, a measurement for the amount of vegetation activity) data plus ground measured temperature and elevation data, to predict the distribution of Glossina morsitans and G. pallidipes in Kenya and Tanzania. Taking the historical fly distribution (Ford & Katondo, 1973) as a reference, satellite derived predictor variables were selected and an accuracy of respectively 84 and 79% correct predictions was obtained when predicting G. morsitans and G. pallidipes presence. For West Africa, Rogers et al. (1996) carried out a similar exercise and produced distribution limits of eight different tsetse species encountered in Burkina Faso and Ivory Coast, at a degree resolution. The satellite data in this study comprised Fourier-processed NDVI, Channel 4 (linked to ground temperature) and CCD values (Cold Cloud Duration, linked to rainfall). As before, historical records served as the reference fly distribution (Laveissière and Challier, 1977, 1981). Selecting the 10 best predictor variables, a and 71% correct description, including all the data in the training set, was obtained when predicting the abundance respectively of G. tachinoides, G. palpalis, G. m. submorsitans and G. longipalpis. In Togo Hendrickx et al. (1993, 1996) and Rogers et al. (1994) introduced discriminant analysis of satellite data to identify tsetse habitat as an attempt to minimise the use of ground collected data and to optimise satellite imagery application. In their final paper Hendrickx et al. (2001) non-linear discriminant analysis models were used in combination with Fourrier processed AVHRR-NOAA predictor data to produce spatial predictions of fly distribution for G. m. submorsitans, G. longipalpis, G. tachinoides, G. p. palpalis, G. fusca and G. medicorum. Obtained results yielded presence/absence accuracies of 90%. Low-Medium-high abundance models were also produced for both riverine species, G. tachinoides (70% correct) and G. p. palpalis (56% correct). Three other aspects linked to vector prediction were also studied: (1) the effects on accuracy of using a spatial sub-sample to predict the remainder of the country, (2) the effects on accuracy of the number of predictor variables included in the models and (3) the accuracy of using trainingsets to predict fly presence in non-adjacent areas. Not surprisingly decreasing the size of the trainingset systematically reduced the accuracy of the predictions. The effect of the number of predictor variables was less straight forward. It was shown that accuracy increased to a maximum with an increasing number of predictor variables for sampled grids included in the training set. However for grids not included in the training set predictions were allways maximised with fewer predictor variables as compared to results obtained in grids included in the training set. This highlighted the risk over overfitting models to restricted sub samples. Finally it was clearly shown that one should be cautious when using training sets to predict fly presence in non-adjacent areas. The huge discrepancies observed between prediction of fly presence in Togo using data from Côte d Ivoire and Burkina Faso and the observed Togo maps clearly suggested that whilst training set quality may certainly play a role, multivariate conditions at the grid level were (are) far to different between those two areas to produce accurate enough results. This work was later extended to Western Burkina Faso in eco-climatical dryer areas complementary to the prevailing conditions in Togo. The aim was to map fly-ecology patterns along the Mouhoun river system (Fig.2 E) as a contribution to the understanding of riverine fly fragmentation patterns at their distribution limits. The developed Togo approach towards georeferenced trypanosomosis management was further extended to Burkina Faso. Results included maps of (1) epidemiological patterns and (2) fly-ecology patterns on the Mouhoun river in Western Burkina Faso (Hendrickx and Tamboura, 2000). In Southern Africa Robinson et al. (1997a, 1997b) analysed the historical distribution of G. m. centralis, G. m. morsitans and G. pallidipes in the common fly belt of Malawi, Mozambique, Zambia and Zimbabwe (Ford and Katondo, 1973) using NDVI, ground measured temperatures, rainfall and elevation. Multivariate techniques included were linear discriminant analysis, maximum likelihood classification and principal component analysis. For each species, the best predictor variables were selected and the discriminant functions applied to produce 84 to 92% correct predictions. Interestingly the analysis successfully identified the geographical limits of both subspecies of G. morsitans present. As for field surveys, remote sensing has been mainly used to assist in mapping the vectors which distribution and abundance is dependant on eco-climatical variables. The sole example of predicting trypanosome distribution and prevalence rates is the above mentioned Togo study. Using similar techniques as described for tsetse spatial prediction models were produced for the prevalence of Trypanosoma congolense and T. vivax (Hendrickx et al. 2000). In addition prediction maps were also produced for average herd packed cell volume (PCV, a measure of aneamia, the most important sympto
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