Summary. Geographical information systems (GIS) facilitate the incorporation of spatial ..... that some degree of separation exists between the areas in which the ...
Rev. sci. tech. Off. int. Epiz., 2002, 21 (1), 91-102
Geographical information systems as a tool in epidemiological assessment and wildlife disease management D.U. Pfeiffer (1) & M. Hugh-Jones (2) (1) Epidemiology Division, Royal Veterinary College, Hawkshead Lane, North Mymms, Hertfordshire AL9 7TA, United Kingdom (2) Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, Louisana 70803-8404, United States of America
Summary Geographical information systems (GIS) facilitate the incorporation of spatial relationships into epidemiological investigations of wildlife diseases. Consisting of data input, management, analysis and presentation components, GIS act as an integrative technology in that a range of very varied data sources can be combined which describe different aspects of the environment of wild animals. The analytical functionality of GIS is still evolving, and ranges from visual to exploratory and modelling methods. Output generated by GIS in map format has the particular advantage of allowing implicit representation of spatial dependence relationships in an intuitive manner. The technology is becoming an essential component of modern disease surveillance systems. Keywords Epidemiology – Geographical information systems – Spatial analysis – Wildlife.
Introduction Investigation of diseases in wild animals presents a special challenge to veterinary epidemiologists as the locations of individual animals within the populations at risk are much less predictable than is the case with domestic animals. This affects the ability to find animals during, for example, cross-sectional studies, and to undertake cohort studies in which the same animal must be examined repeatedly and therefore recaptured reliably. The presence of wild animals in space, while difficult to predict, is dependent on environmental and geographical factors. Both types of information represent one of the cornerstones of geographic information systems (GIS). This is one of the reasons why GIS technology has already become an essential tool for wildlife management and research.
Epidemiological investigations gain strength from being able to incorporate information about the proximity relationships between animals at risk, and also about the context relating to the spatial distribution of risk factors. Recognising the importance of space and the associated challenge, ecologists have named it ‘The final frontier for ecological theory’ (18). Geographical information systems are made up of a number of components as shown in Figure 1, and these will be discussed later. Although originally an independent science, the study of GIS is now being slowly absorbed into information science (IS). This is a sensible development, since IS is the common source for all data. A principal function of GIS is to augment the senses with information which is not immediately accessible from inspecting tabular data. Over the last decade, the technology has become easier to use, and at the same time the quantity of
© OIE - 2002
92
Input Acquisition
Rev. sci. tech. Off. int. Epiz., 21 (1)
➡
Data management Quality control
➡
Presentation/ Communication Visualisation
➡
Analysis Visualisation Exploration Modelling Verification
Fig. 1 Components of geographical information systems
available data has increased exponentially, whereas the quality of the data has not improved at the same pace. Field verification of the results of GIS analyses is therefore of paramount importance. While the output is usually an image or graphic representing the digital information, this is only to facilitate easy and rapid understanding. For this reason, the graphics must be honest, as graphics can aid mendacity.
Medical/veterinary geographical information systems Decision-making in relation to human or animal health problems involves a triad of decision loci, as follows: a) case finding b) risk assessment c) control programme delivery. Classically, the epidemiological focus has been on predominantly the aspatial characteristics of these loci, which can be measured and quantified independently of space (e.g. age, breed, sex, time). With the advent of GIS, the three loci are now recognised to have spatially interactive characteristics. The simplest use of GIS is data visualisation, a map, of the known cases. This allows questions such as ‘where are the environments considered relevant to the disease or health risk?’. On further consideration of the cases, additional questions can be asked, such as ‘where are the cases that have not been found?’; ‘where are the next cases?’; ‘where are the cases that are not occurring or were prevented and which must be confirmed as absent?’. Denominators are built into the data, allowing rates to be easily calculated and plotted. Case prediction, whether positive or negative, leads towards an interactive function of GIS. In this context, risk mapping links observed case occurrence with risk, similarly to field laboratories whose
© OIE - 2002
location can be optimised with transport theory and other tools, but must take into account population density, infrastructure, incidence, prevalence, equipment maintenance, communications, and staff housing, to name but a few factors. Control programme optimisation is encountered further into the analyses when programme response and evaluation are included.
Geographical information system technology A GIS is the result of integrating various different technologies, data sources and interest groups for the purpose of collecting, storing, analysing, presenting and disseminating geographically-referenced information (i.e. spatial data). The increasing importance of GIS is strongly correlated with the rise of the information age, particularly the development of powerful computing technology. Spatial data is defined as geographical features and the attributes of these features. The features can be points, lines or polygons, which can be used, for example, to represent the locations of animals, rivers or forest patches, respectively. Each individual geographical feature can be linked to specific attribute information. Such attributes could be, for example, the disease status and the age of each animal, the name of the river, or the predominant tree species in the forest patch. Each feature will often have multiple attributes. To manage spatial data, a GIS requires both spatial and nonspatial database management functionality. The geographical features are managed by the spatial data functions, which also maintain links to the attribute data. The latter are often stored in a ‘standard’ database management system (DBMS). The strength of this linkage varies between different GIS software packages, but a true GIS should allow spatial querying of the attribute database and thereby allow examination and management of different attributes taking into account the proximity of spatial features. This would allow, for example, the calculation of distances between infected animals, or estimation of the distances from diseased animals to the nearest river. Such functionality can only be handled using geographical co-ordinates in a standard DBMS. Fortunately, extensions to DBMS can correctly manipulate geographic co-ordinates as well as the intersection, clipping or union of polygons and vectors.
Data collection Spatial feature information can be imported into a GIS using a number of different methods. Geographical co-ordinate information can be manually digitised, read from paper maps, or determined directly in the field using global positioning systems (GPS). An additional conversion step is required if images are generated as an intermediate step, such as when
93
Rev. sci. tech. Off. int. Epiz., 21 (1)
paper maps are scanned or landscapes are remotely sensed using aeroplanes or satellites. However, the use of georeferenced information which is already available in a digital format is often possible. Data quality is one of the principal concerns which every GIS user must deal with. Although this is also an issue for nonspatial data to be analysed, GIS has the additional requirement of spatially- and temporally-accurate recording of geographical position. Frequently, a spatially-accurate map of the different vegetation types in a study area may be available, but the map is not recent enough to represent the current situation. As a result, the GIS user will often work with a spatial dataset that is the result of combining geo-referenced information obtained from different sources with potentially widely varying data quality. In addition, the resolution of the digital map must be appropriate to the habitat of the home range, the age and seasonal activity patterns and the disease epidemiology. For example, the activity patterns of insectivorous bats and woodland birds are very different even if their roosting sites are adjoining; some trematode habitats can be measured in metres, while others are best visualised in square kilometres. Although population sampling of human or livestock populations which are readily accessible is a relatively simple statistical exercise, this is not the case for wildlife. In simple terms, the problem is to match animal availability with age, season, disease activity and statistical precision. For example, hunter-gathered samples, such as foxes, are usually heterogeneous because hunter-sampled areas are not randomly distributed; hunters differ in enthusiasm to submit carcasses, leading to under- and over-representation; and whole families of foxes may be present in an area between February and June, when they live close together (in the case of Echinococcus multilocularis, infection can be familial and thus family members are not independent units of any sample) (30). Another problem with samples gathered by hunters is that older animals are under-represented. When catching (and releasing) wild armadillos (Dasypus novemcinctus), juveniles are readily caught but are rarely infected with Mycobacterium leprae. In southern Louisiana, 30% of adults can be infected, but only until significant numbers have been caught does the non-clustering of the infection become clear (33, 25). In contrast, if foxes are sought during July to September when the family groups have broken up, the E. multilocularis infections in juveniles will have maximised, thereby aiding status recognition and diagnosis (under endemic conditions, juveniles are more often infected than adults). If only sporadic cases occur, the rates for the two groups are similar. Clearly, attempts must be made to develop a sampling scheme which results in equal representation of all areas as far as possible.
Data storage Important differences exist between GIS software packages with respect to the spatial data model used to store spatial feature information. Spatial data are usually divided into layers describing different types of information. For example, the different locations in which a radio-tagged animal has been recorded during a study would be stored as one layer, and the vegetation types in the study area as another layer. The spatial data represented by a particular layer can be stored using the raster or the vector model. In the raster model, space is represented as a regular grid in which the geographical space covered by an individual grid cell defines a spatial feature. This means that within a layer, each of the different locations in which the aforementioned animal has been radio-tracked will be represented by a rectangular grid cell, and the size of the grid cells will therefore control the resolution and accuracy of the spatial data layer. This layer will then have a given number of grid cells depending on the resolution which was selected, and each grid cell will store the frequency of the presence of the radio-tracked animal as values between zero and the maximum number of records within the space defined by an individual grid cell. In contrast, the vector data model defines spatial features, such as points, lines and polygons by a combination of points linked by lines (arcs). In the case of a radio-tracked animal, each location will be recorded as a point, and the layer therefore constitutes a series of x-y co-ordinates. This means that spatial data recorded using the vector model are inherently more accurate compared to those recorded using the raster model. However, the raster model has the advantage that it is methodologically easier to store data, to represent continuous phenomena such as rainfall and to perform spatial operations between different data layers. The majority of GIS software packages can now work with both data models, but the ability to perform analytical spatial operations involving both raster and vector layers simultaneously is limited.
Data analysis The analysis of GIS data can be broadly categorised into visualisation, exploration and modelling. Most GIS users will conduct principally visual analyses. The type of map presentation depends on the type of data available, either the actual event locations (such as the x-y co-ordinates of the location in which an animal was sighted), or aggregate data (where the number of animals within an area has been counted). Choropleth maps utilising administrative units with artificial boundaries are often used to present aggregate information. In this type of map, a shade or colour is assigned to the administrative areas, thereby visualising the value of the variable of interest. The hatching pattern or colour is based on a class interval or continuous scale derived from a descriptive statistic of the aggregated data, such as the prevalence of disease. In population surveys without geo-referencing of individual samples, this is often the only feasible way to present
© OIE - 2002
94
Rev. sci. tech. Off. int. Epiz., 21 (1)
the spatial distribution of samples. Figures 2a and 2b describe the spatial distribution of the prevalence of tuberculosis in wild badgers in Great Britain, which were examined as part of badger removal operations between 1974 and 1997. The data is based on information recorded in the Central Veterinary Laboratory ‘Ecology’ (CVL‘E’) Wildlife Unit Badger Database. The two maps represent two different methods of illustrating prevalence data. Compared to Figure 2a, Figure 2b has the advantage of also incorporating information about the relative magnitude of the total number of badgers examined, by scaling the pie charts appropriately. When inspecting choropleth maps, the following factors should be taken into consideration: a) the boundaries of administrative or similar constructs are often chosen for political or other reasons irrelevant to disease spread, although such boundaries may have a direct impact on the reporting of disease b) sample size is frequently ignored when spatial data are presented. Thus, while neighbouring areas may appear to have different prevalence levels, the confidence limits may overlap and the differences are therefore more likely to be random
c) as a result of the above, mapping disease data in this manner may lead to false interpretations of disease clusters or diseasefree zones (22) d) all relationships observed between variables may only hold in that particular aggregation of data; this is known as the modifiable areal unit problem with specific reference to differences between natural and artificial area constructs and the ecological fallacy in epidemiology of applying inferences gained at a higher level to a lower level of aggregation (13, 16). A range of methods has been developed to deal with these problems (6, 12, 27). These methods include Kriging, by interpolating values between the centroids of each area (24); pycnophylactic interpolation, by iteratively interpolating a continuous surface from data given by irregular geographic polygons (32); use of a moving average filter (5); statistically controlled successive unification of neighbouring units (STACSUNU) on the assumption that neighbouring units whose sampling results do not justify statistical distinction are merged; and lastly, use of a Bayesian model (20). With any of the described methods which are based on a statistical model, it is important to assess the residuals, and be aware of edge effects (21).
a)
b)
County boundaries Prevalence of tuberculosis infection 0 0-0.03 0.03-0.1 0.1-0.11 0.11-0.16 0.16-0.18 0.18-0.21 0.21-0.29 0.29-0.5 No data
County boundaries Positive for tuberculosis Negative for tuberculosis
Fig. 2 Prevalence of tuberculosis in wild badgers in Great Britain examined during badger removal operations over the period 1974-1997, aggregated by county Radii of pie charts scaled according to the size of badger population examined; note that no pie charts are presented for counties without data
© OIE - 2002
95
Rev. sci. tech. Off. int. Epiz., 21 (1)
Point data may require additional manipulation to facilitate meaningful visualisation. Figure 3a presents an example of a simple visualisation of point data based on a longitudinal study of bovine tuberculosis in Australian brushtail possums (Trichosurus vulpecula) in New Zealand (26). The map shows the capture locations of tuberculous wild possums and the locations of all traps, draped over a 10 m contour map. Figure 3b presents a digital terrain model of the study area with a choropleth representing Thiessen polygons draped over it. The Thiessen polygons are used to indicate the areas which have been used by tuberculous possums and different colours indicate which areas were used by possums infected with the four different restriction endonuclease analysis (REA) types identified in the area. Visual examination of this map suggests that some degree of separation exists between the areas in which the different REA types occur. With increased sophistication of computerised technology, three-dimensional map representations have become very easy to produce. Introduction of visual bias is particularly easy when creating such maps, for example through the use of vertical
exaggeration factors, or choice of specific presentation perspective and azimuth. However, appropriate use of the third dimension can substantially increase the visual impact during presentation, and if combined with interactive viewing, can become an effective tool for visual thinking as well as visual communication. Figure 3c shows the density of traps per 40 m2 in the study area as a third dimension which was generated using kernel smoothing from point locations of traps (2). This indicates that trapping intensity was heterogeneous across the study area. In this particular study, radio transmitters were attached to the animals to locate den sites, and Figure 3d shows smoothed representations of the density of den sites based on all den sites and those which were used by tuberculous possums. The two maps suggest that the area with highest tuberculosis density was not the same as the area with highest den density. This also could have been demonstrated by calculating the ratio of both maps, but this would not have shown the actual magnitude of the numerator and denominator values. When examining Figure 3, it is important to keep in mind the ways in which presentation can be manipulated to emphasise particular aspects.
a) Contour map with trap locations and traps in which tuberculous possums were captured
b) Digital terrain model with Thiessen representation of locations (traps and dens) used by tuberculous possums with different REA types
Locations of tuberculosis captures Trap locations 10 m contour lines
c) Smoothed representation of trap density
0
100
200
300
400 m
REA type 10 REA type 4b REA type 4 REA type 4a Digital terrain model
0
100
200
300
400
500 m
d) Visual comparison of smoothed den and tuberculosis den density
REA : restriction endonuclease analysis
Fig. 3 Different types of map representations using data from a longitudinal study of tuberculosis in a wild possum population in New Zealand
© OIE - 2002
96
Rev. sci. tech. Off. int. Epiz., 21 (1)
The estimation of home ranges of wild animals represents a usage of GIS for visual and descriptive analysis which is specific to wildlife research. Ecological research has resulted in the development of a large number of home range estimators, and GIS can be used to specifically investigate the overlap between home ranges of wild animals, which will be useful for investigating disease spread within populations that are closely monitored. Figure 4 presents the 95% kernel estimates of home range from the longitudinal study of four possums which were infected with M. bovis; this is draped over a vegetation map generated from a classified satellite image (36). The map shows the extent of the overlap of the home ranges, and at the same time indicates that these possums were principally moving around in habitat covered by manuka bush vegetation.
a) Minimum convex polygon
b) Harmonic mean 95%
c) Kernel density estimator 95% probability isopleth
Fig. 6 A comparison of different techniques for the analysis of home range using the same data set from an adult fox over a period of one month (n = 154) Source: Staubach et al., 2000 (28)
Vegetation types
0.6 Size of homerange (km2)
Unclassified Pine Beech Shrubland Podocarp Manuka Pasture Manuka/gorse Manuka/pasture
0.5 0.4 0.3 0.2 0.1
Fig. 4 Home ranges (95% kernel estimates) of four tuberculous possums, draped over a classified vegetation map derived from a remotely sensed satellite image
However, some caution should be exercised; the home range of an animal is a function of age, sex, season and habitat, and in addition, the confounding problems of hunting, contact rates, grid sizes, numbers of telemetry fixes, times of those fixes, and subsequent analytical methods must be considered. Different algorithms produce different home ranges using the same data. These various points are demonstrated in Figures 5, 6 and 7 from Staubach et al. (28). While problematic when designing a single study, these differences are even more difficult to deal with when comparing results of different published studies (17). a) 3 months (n = 65)
b) 6 months (n = 174)
c) 9 months (n = 216)
Fig. 5 Illustration of the increase of home range size of a fox according to age Source: Staubach et al., 2000 (28)
© OIE - 2002
20
40
60
80
100 120
140
160
180
200
Number of fixes
Fig. 7 Effect of the number of radio fixes on the estimate of the home range size of a sub-adult fox using the minimum convex polygon method (n = 257; number of bootstrap replications = 100) Source: Staubach et al., 2000 (28)
A unique feature of GIS technologies is the capability to generate new geographic data layers which are based on overlaying different thematic layers. The layers can be linked through Boolean logic, weighted combinations or probabilistic relationships (3). Exploratory analysis involves statistical examination of the data for the presence of any patterns. Such analysis is not used to test causal hypotheses. Depending on the type of data used, different methods can be applied, aimed at aggregated data (such as counts per area), or actual point locations. These methods produce either general statistics indicating that a spatial cluster exists within the area, or local statistics showing the location of any clusters within the area investigated. The Cuzick-Edward’s statistic for case-control data is an example of a global statistic (9), as is the k-function which produces a graphical presentation of the expected density of point locations depending on distance (1). With point data, the
Rev. sci. tech. Off. int. Epiz., 21 (1)
spatial scan statistic (19) allows identification of the location and the extent of spatial clusters (Figs 8a and 8b). In the example dataset, a most likely cluster was identified in the area covered by the traps in the northern part of the study area, which confirms the hypothesis derived from visual inspection of Figure 3d. Aggregated data, such as the prevalence of infected animals per area construct, can be examined for the presence of spatial clustering using the Moran or Geary coefficients for spatial auto-correlation. The spatial scan statistic can also be used to obtain a statistic which will identify the location of likely clusters (19). More recently, GIS technology has been used to describe the landscape characteristics of the habitat used by animal species. This is a new concept in that it involves generating summary statistics describing the habitat, particularly in terms of its fragmentation and the mixture of vegetation types (15). The particular relevance of this methodology comes from the
a) Locations used by possums with (red) and without (yellow) tuberculosis, draped over a digital elevation model of the study area
b) Locations of clusters identified using the spatial scan statistic (red: primary cluster; yellow: secondary cluster; blue: secondary cluster)
Fig. 8 Spatial cluster investigation of tuberculosis in wild possums
97
realisation that the presence of wild animal species is likely to be influenced by environmental factors which are present within a typical home range. Definition of the presence and absence of any individual factor independently is not sufficient, the spatial heterogeneity is the factor which may affect the ability of an animal species to survive in such an area (14). This was pointed out in the context of fox rabies by Tinline, who suggested that the survival of rabies virus in a fox population may require a certain mixture of forest/pasture habitats (31). Similarly, foxes infected by E. multilocularis are more frequently found near water, in areas of high soil humidity, and on pastures, but under-represented in forest areas, suggesting that dryness may limit the viability of E. multilocularis oncospheres (29). Such information about landscape fragmentation can then be used as an additional predictor of likely disease presence within a regression modelling framework, but as with all other modelled predictions, it only has value when verified by subsequent field studies. Spatial statistical disease modelling is aimed at investigating hypothesised causal effects which are considered to be associated with the occurrence of disease clusters. Such models will become useful for the management of wild animal disease if they have acceptable predictive accuracy. Myers et al. describe the use of risk mapping systems to forecast disease epidemics affecting humans (23). In the future, such systems could also be developed to predict wildlife diseases. Regression algorithms which are sufficiently robust to deal with the dependence structure of spatial data have recently been developed, but are only available when using specialised computing tools. Bayesian random effects regression models can now be used to spatially model the relative risk of infection, incorporating any important covariates (20, 35). Geographical information systems have already become an essential tool for decision making, and the integration of multicriteria and multi-objective decision analysis functions provides an additional set of tools (11). These methods allow the generation of decision rules incorporating both qualitative and quantitative information, and take into account the costs as well as the benefits of these decisions. The weights of evidence method is a quantitative approach for combining evidence in support of a hypothesis (3). Although developed for mineral potential mapping, this method can also be used to spatially predict the probability of the presence of diseased animals given a weighted combination of predictor layers. In multi-criteria decision-making, fuzzy criteria can also be used, or criteria uncertainty can be taken into consideration. This methodology could be used, for example, when deciding where to place vaccine bait in the case of wild animal vaccination campaigns.
© OIE - 2002
98
Data presentation and dissemination One of the particular strengths of GIS is the presentation functionality of the technology. Maps can be generated which are tailored to specific requirements. These maps can represent several types of attribute information in the same map, which can be two or three dimensional. Figure 3 shows a selection of such presentation methods. Digital video can be produced to illustrate the temporal dynamics of infection across a landscape. Interactive presentations can also be generated, which can then be made widely accessible via the Internet. However, it is important to bear in mind that map presentation, just as any other type of graphical representation, with the help of inexpensive computing and ingenious techniques for image processing, provides endless opportunities for mischief (34).
Geographical information systems in wildlife disease research The majority of wildlife disease research involving the use of GIS is associated with diseases for which wildlife represent a reservoir of infection for domestic animals or humans, such as bovine tuberculosis, West Nile virus and rabies. Wild badgers and brushtail possums are considered to be reservoirs of bovine tuberculosis in the United Kingdom and New Zealand, respectively. Delahay et al. made extensive use of GIS and spatial analysis to describe the spatio-temporal dynamics of bovine tuberculosis in an intensively studied badger population (10). The use of these methods led to the important conclusion that infection foci remain very stable over time within specific social groups, and spread only very slowly to neighbouring groups. A predictive model of 80% accuracy was developed by Boone to determine the serological presence of Sin Nombre virus infection in deer mice (Peromyscus maniculatus) in Walker River Basin, Nevada and California in the United States of America (USA) (4). The model was derived using multiple discriminant analysis on the basis of remote sensing and GIS data. The interaction between disease in wild and domestic animals or humans is one of the areas where extensive use can be made of GIS methodology and spatial analysis (7). In this context, quantification of the potential for direct or indirect contact between the two species is often necessary. While fairly accurate information may be available about the spatial presence of domestic animals, that same information will be much more uncertain for wild animals, but GIS can provide the necessary tools to enable such analyses.
© OIE - 2002
Rev. sci. tech. Off. int. Epiz., 21 (1)
Geographical information systems in wildlife disease surveillance Animal disease surveillance is aimed at monitoring endemic, epidemic, emerging and new diseases. A technically difficult and costly undertaking for domestic animals, such surveillance is an even more challenging task for wild animals. In situations where animal species serve as reservoirs or vectors, surveillance mechanisms have existed for a long time. For example, most countries world-wide record the occurrence of wildlife rabies. Usually, this is only performed at some level of spatial aggregation, such as the district or national level. More effective systems record the actual locations in which cases have been reported, and thereby allow more detailed investigations with respect to spatio-temporal dynamics. Wildlife disease surveillance for new and emerging diseases is always likely to receive a low priority because of the cost implications associated with effective systems. It therefore must be accepted that such systems may have only limited sensitivity for detecting such diseases, at least initially. In the case of West Nile virus, surveillance activities have the highest sensitivity for human and domestic animal cases, but very poor sensitivity for wild animal species such as wild birds. Similarly, areas distant from clinics and/or laboratories infrequently or never submit samples; this has been a problem when determining the true status of coyote rabies in west Texas and the success of the oral vaccination programme. Wildlife disease surveillance data generally suffer from a lack of denominator information. The data are also likely to be affected by strong reporting bias, and therefore must be interpreted with great caution. Figure 9 presents an example of this problem; absence of reports of rabies in jackals from a particular country does not necessarily mean absence of cases of rabies in jackals.
Cases of rabies in jackals 0 1-7 8-20 21-28 29-146
Fig. 9 Cases of rabies in jackals in Africa, parts of Europe and Asia, as reported to the World Health Organization in 1994 (dotted areas did not report rabies in any animal species)
99
Rev. sci. tech. Off. int. Epiz., 21 (1)
At the sub-national level, geo-referenced data on rabies occurrence has been recorded by the Department of Livestock Services, Harare, Zimbabwe, for over ten years. This information has been very useful for descriptive purposes, but particularly in the case of wildlife data, the absence of denominator information and the unquantifiable potential for reporting-bias result in mapped presentations of case numbers being difficult to interpret; for an example, see Figure 10a. Analyses can be conducted amongst diagnosed cases, as shown in Figure 10b, as long as reporting is not affected by differential selection bias. In this particular map, the pie charts show that cases of rabies in jackals represented the majority of rabies diagnoses in the north of the country, and at the same time this is the location in which most animal rabies was reported.
The interpretation of wildlife disease surveillance can be enhanced by investigation of spatial patterns of occurrence. Curtis describes the use of information about the spatial heterogeneity of laboratory submissions to identify counties with inadequate reporting in the State of Kentucky, USA (8). The method is based on a proximity filter which works by comparing the actual number of submissions in an area (such as a county area or a circle) to the distribution of cases surrounding that area. A randomisation procedure is used to compare the actual number of submissions against an expected distribution. If a significantly low number is found, investigations can be undertaken to discover the reasons for this (e.g. unsuitable terrain, low human population, animals not reported properly by local officers).
a) Laboratory-confirmed cases
Conclusions
No. of cases 0 0-5 6-13 14-36 37-82
b) Proportion of cases in jackals (red) amongst all confirmed animal rabies diagnoses (green); pie charts scaled according to total number of rabies diagnoses
Fig. 10 Spatial distribution of cases of rabies in jackals in Zimbabwe between 1991 and 1993, aggregated by administrative unit level 3 Data provided by Dr J. Bingham, Department of Livestock Services, Harare, Zimbabwe
Geographical information systems are admirably suited to wildlife studies if only because these animals are mobile. Wild animals do not live and die within arbitrarily fixed boundaries as do livestock and companion animals. The bounded areas of wildlife are fungible, depending on species, age, sex and season, as well as cover and food availability. Through the use of maps and similar spatial visualisations, a process of visual thinking is encouraged, which allows the human brain to rapidly absorb and interpret information and make further connections. However, this can also be abused through the type of presentation chosen (colours, three-dimensional) and bias in the calculations and choice of data. Modern computing technology now allows GIS to work with very large, multidimensional datasets. Thus, GIS can use analytical methods that previously could not be imagined and can perform these methods rapidly and repeatedly. This allows mental/intellectual exploration of the whole ecological situation, but especially in space and time, which are the most important dimensions of the diseases of wildlife. Nevertheless, a note of caution is necessary. Just as burgeoning abuse of statistical analysis was observed when desk-top computers with easy-to-use statistics software became available to all, so similar misuses and inappropriate utilisations of GIS analytical methods are currently seen. Therefore, any appraisal of a GIS report on wildlife diseases must question whether the correct data and analyses have been used.
■
© OIE - 2002
100
Rev. sci. tech. Off. int. Epiz., 21 (1)
Les systèmes d’information géographique appliqués à l’évaluation épidémiologique et à la gestion des maladies de la faune sauvage D.U. Pfeiffer & M. Hugh-Jones Résumé Les systèmes d’information géographique (SIG) permettent d’intégrer les analyses spatiales dans les enquêtes épidémiologiques sur les maladies de la faune sauvage. Ces systèmes comprennent une série de modules permettant la saisie, la gestion, l’analyse et la présentation des données ; les SIG sont une technologie d’intégration, c’est-à-dire qu’ils permettent de combiner des informations provenant de sources extrêmement variées et décrivant différents aspects de l’environnement de la faune sauvage. La fonction analytique des SIG est, encore aujourd’hui, en constante évolution, et utilise des méthodes visuelles, exploratoires ainsi que des techniques de modélisation. Les données cartographiques générées par les SIG offrent l’avantage de permettre une représentation implicite, et perceptible intuitivement, des relations d’interdépendance spatiale. La technologie devient une composante essentielle des systèmes modernes d’épidémiosurveillance. Mots-clés Analyse spatiale – Épidémiologie – Faune sauvage – Systèmes d’information géographique. ■
Los sistemas de información geográfica como instrumento de evaluación epidemiológica y gestión de las enfermedades de la fauna salvaje D.U. Pfeiffer & M. Hugh-Jones Resumen Los sistemas de información geográfica (SIG) facilitan la integración de relaciones espaciales en la investigación epidemiológica sobre enfermedades de la fauna salvaje. El SIG, constituido por una serie de módulos de entrada, gestión, análisis y presentación de los datos, es una tecnología integradora en la medida en que permite combinar información de origen muy diverso para describir diferentes aspectos del medio en el que viven los animales salvajes. Las prestaciones analíticas de los SIG, cada vez más perfeccionadas, abarcan desde los métodos visuales a los de exploración y de elaboración de modelos. La salida cartográfica que genera un SIG presenta la ventaja de poder reproducir implícitamente, y de forma intuitiva, relaciones de dependencia espacial. La tecnología está en vías de convertirse en un componente básico de los sistemas modernos de vigilancia sanitaria. Palabras clave Análisis espacial – Epidemiología – Fauna salvaje – Sistemas de información geográfica. ■
© OIE - 2002
Rev. sci. tech. Off. int. Epiz., 21 (1)
101
References 1. Bailey T.C. & Gatrell A.C. (1995). – Interactive spatial data analysis. Longman Group, Harlow, Essex, 413 pp. 2. Bithell J.F. (1990). – An application of density estimation to geographical epidemiology. Stat. Med., 9, 691-701. 3. Bonham-Carter G.F. (1994). – Geographic information systems for geoscientists: modelling with GIS. Elsevier Science, Oxford, 398 pp. 4. Boone J.D., McGwire K.C., Otteson E.W., DeBaca R.S., Kuhn E.A., Villard P., Brussard P.F. & St. Jeor S.C. (2000). – Remote sensing and geographic information systems: charting Sin Nombre virus infections in deer mice. Emerg. infect. Dis., 6 (3), 248-258. 5. Burrough P.A. & McDonnell R.A. (1998). – Principles of geographical information systems. Oxford University Press, Oxford, 333 pp. 6. Carrat F. & Valleron A.-J. (1992). – Epidemiologic mapping using the ‘Kriging’ method: application to an influenza-like illness epidemic in France. Am. J. Epidemiol., 135 (11), 1293-1300. 7. Clifton-Hadley R.S. (1993). – The use of a geographical information system (GIS) in the control and epidemiology of bovine tuberculosis in south-west England. In Proc. Annual Meeting of the Society for Veterinary Epidemiology and Preventive Medicine (M. Thrusfield, ed.), 31 March-2 April, Exeter. University of Edinburgh, Edinburgh, 166-179. 8. Curtis A. (1999). – Using a spatial filter and a geographic information system to improve rabies surveillance data. Emerg. infect. Dis., 5 (5), 603-606. 9. Cuzick J. & Edwards R. (1990). – Spatial clustering for inhomogeneous populations. J. roy. Statist. Soc., B, 52 (1), 73104. 10. Delahay R.J., Langton S., Smith G.C., Clifton-Hadley R.S. & Cheeseman C.L. (2000). – The spatio-temporal distribution of Mycobacterium bovis (bovine tuberculosis) infection in a highdensity badger population. J. anim. Ecol., 69 (3), 428-441. 11. Eastman J.R., Jin W., Kyem P.A.K. & Toledano J. (1995). – Raster procedures for multi-criteria/multi-objective decisions. Photogram. Eng. Rem. Sens., 61 (5), 539-547. 12. Elliott P., Martuzzi M. & Shaddick G. (1995). – Spatial statistical methods in environmental epidemiology: a critique. Stat. Methods Med. Res., 4, 137-159. 13. Fotheringham A.S. & Wong D.W.S. (1991). – The modifiable areal unit problem in multivariate statistical analysis. Environ. Planning A, 23, 1025-1044. 14. Gough M.C. & Rushton S.P. (2000). – Application of GISmodelling to mustelid landscape ecology. Mammal Rev., 30 (3/4), 197-216. 15. Gustafson E.J. (1998). – Quantifying landscape spatial pattern: what is the state of the art? Ecosystems, 1, 143-156.
16. Haining R. (1998). – Spatial statistics and the analysis of health data. In GIS and health (A.C. Gatrell & M. Löytönen, eds). Taylor & Francis, London, 29-47. 17. Harris S., Cresswell W.J., Forde P.G., Trewhalla W.J., Woollard T. & Wray S. (1990). – Home-range analysis using radiotracking data – a review of problems and techniques particularly as applied to the study of mammals. Mammal Rev., 20 (2/3), 97-123. 18. Kareiva P. (1994). – Space: the final frontier for ecological theory. Ecology, 75 (1), 1. 19. Kulldorff M. & Nagarwalla N. (1995). – Spatial disease clusters: detection and inference. Stat. Med., 14, 799-810. 20. Lawson A.B. (2001). – Statistical methods in spatial epidemiology. John Wiley & Sons, Chichester, 277 pp. 21. Lawson A.B. & Williams F.L.R. (2001). – An introductory guide to disease mapping. John Wiley & Sons, Chichester, 133 pp. 22. Monmonier M. & de Blij H.J. (1996). – How to lie with maps. University of Chicago Press, Chicago, 222 pp. 23. Myers M.F., Rogers D.J., Cox J., Flahault A. & Hay S.I. (2000). – Forecasting disease risk for increased epidemic preparedness in public health. In Remote sensing and geographical information systems in epidemiology (S.I. Hay, S.E. Randolph & D.J. Rogers, eds). Academic Press, San Diego, 309-330. 24. Oliver M.A. & Webster R. (1990). – Kriging: a method of interpolation for geographical information systems. Int. J. Geogr. Inf. Syst., 4 (3), 313-332. 25. Paige C. (1998). – Estimation of incidence density of Mycobacterium leprae infection within a population of wild nine-banded armadillos (Dasypus novemcinctus) [dissertation]. Louisiana State University, Baton Rouge, 82 pp. 26. Pfeiffer D.U., Morris R.S., Harris A., Jackson R. & Paterson B. (1991). – The epidemiology of bovine tuberculosis in the common brushtail possum. In Proc. 6th International Symposium for Veterinary Epidemiology and Economics (S.W. Martin, ed.), 12-16 August, Ottawa. Department of Population Medicine, Ontario Veterinary College, University of Guelph, Ontario, Canada, 430-433. 27. Smans M. & Esteve J. (1992). – Practical approaches to disease mapping. In Geographical and environmental epidemiology: methods for small-area studies (P. Elliott, J. Cuzick, D. English & R. Stern, eds). Oxford University Press, Oxford, 141-150. 28. Staubach C., Stiebling U., Ziller M., Tackmann K., Thulke H.H. & Schlüter H. (2000). – The consequences of using different analysis techniques on wildlife study data to model disease transmission. In Proc. 9th Symposium of the International Society of Veterinary Epidemiology and Economics (ISVEE) (M.D. Salman, P.S. Morley & R. Ruch-Gallie, eds), 6-11 August, Breckenridge, Colorado. ISVEE, Department of Environmental Health, Colorado State University, Fort Collins, 885-887.
© OIE - 2002
102
29. Staubach C., Thulke H.-H., Tackmann K., Hugh-Jones M.E. & Conraths F.J. (2001). – Geographic information system-aided analysis of factors potentially influencing the spatial distribution of Echinococcus multilocularis infections of foxes. Am. J. trop. Med. Hyg., 65 (6), 943-948. 30. Tackmann K., Selhorst T., Staubach C. & Conraths F.J. (2001). – Epidemiological approaches in the study of Echinococcus multilocularis infection in foxes. In World Health Organization/Office International des Epizooties Manual on echinococcosis in humans and animals: a public health problem of global concern (J. Eckert, M.A. Gemmell, F.-X. Meslin & Z.S. Pawlowski, eds). Office International des Epizooties, Paris, 182-188. 31. Tinline R.R. (1988). – Persistence of rabies in wildlife. In Rabies (J.B. Campbell & K.M. Charlton, eds). Kluwer Academic Publishers, Boston, 301-322. 32. Tobler W.R. (1979). – Smooth pycnophylactic interpolation for geographic regions. J. Am. vet. med. Assoc., 74, 519-535.
© OIE - 2002
Rev. sci. tech. Off. int. Epiz., 21 (1)
33. Truman R.W., Kumaresan J.A., McDonough C.M., Job C.K. & Hastings R.C. (1991). – Seasonal and spatial trends in the detectability of leprosy in wild armadillos. Epidemiol. Infect., 106 (3), 549-560. 34. Tufte E.R. (1997). – Visual explanations – images and quantities, evidence and narrative. Graphics Press, Cheshire, Connecticut, 156 pp. 35. Wakefield J.C., Best N.G. & Waller L. (2000). – Bayesian approaches to disease mapping. In Spatial epidemiology – methods and applications (P. Elliott, J.C. Wakefield, N.G. Best & D.J. Briggs, eds). Oxford University Press, Oxford, 104127. 36. Worton B.J. (1989). – Kernel methods for estimating the utilization distribution in home-range studies. Ecology, 70 (1), 164-168.
