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IntroductionInvestigation of diseases in wild animals presents a specialchallenge to veterinary epidemiologists as the locations of individual animals within the populations at risk are much lesspredictable than is the case with domestic animals. This affectsthe ability to find animals during, for example, cross-sectionalstudies, and to undertake cohort studies in which the sameanimal must be examined repeatedly and therefore recapturedreliably. The presence of wild animals in space, while difficult topredict, is dependent on environmental and geographicalfactors. Both types of information represent one of thecornerstones of geographic information systems (GIS). This isone of the reasons why GIS technology has already become anessential tool for wildlife management and research.

Epidemiological investigations gain strength from being able toincorporate information about the proximity relationshipsbetween animals at risk, and also about the context relating tothe spatial distribution of risk factors. Recognising theimportance of space and the associated challenge, ecologistshave 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 discussedlater. 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 senseswith information which is not immediately accessible frominspecting tabular data. Over the last decade, the technologyhas become easier to use, and at the same time the quantity of

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Geographical information systems as a tool in

epidemiological assessment and wildlife diseasemanagement

SummaryGeographical information systems (GIS) facilitate the incorporation of spatialrelationships into epidemiological investigations of wildlife diseases. Consistingof data input, management, analysis and presentation components, GIS act as anintegrative technology in that a range of very varied data sources can becombined which describe different aspects of the environment of wild animals.The analytical functionality of GIS is still evolving, and ranges from visual toexploratory and modelling methods. Output generated by GIS in map format has the particular advantage of allowing implicit representation of spatialdependence relationships in an intuitive manner. The technology is becoming anessential component of modern disease surveillance systems.

KeywordsEpidemiology Geographical information systems Spatial analysis Wildlife.

D.U. Pfeiffer(1)& M. Hugh-Jones(2)

(1) Epidemiology Division, Royal Veterinary College, Hawkshead Lane, North Mymms, Hertfordshire AUnited Kingdom(2) Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, BRouge, Louisana 70803-8404, United States of America

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available data has increased exponentially, whereas the qualityof the data has not improved at the same pace. Field verificationof the results of GIS analyses is therefore of paramountimportance. While the output is usually an image or graphicrepresenting the digital information, this is only to facilitate easyand rapid understanding. For this reason, the graphics must behonest, as graphics can aid mendacity.

Medical/veterinary geographicalinformation systemsDecision-making in relation to human or animal healthproblems 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, whichcan be measured and quantified independently of space (e.g.age, breed, sex, time). With the advent of GIS, the three loci arenow recognised to have spatially interactive characteristics. Thesimplest use of GIS is data visualisation, a map, of the knowncases. This allows questions such as where are theenvironments considered relevant to the disease or health risk? .On further consideration of the cases, additional questions canbe asked, such as where are the cases that have not beenfound?; where are the next cases? ; where are the cases that arenot occurring or were prevented and which must be confirmedas absent?. Denominators are built into the data, allowing ratesto be easily calculated and plotted. Case prediction, whetherpositive or negative, leads towards an interactive function of GIS. In this context, risk mapping links observed caseoccurrence with risk, similarly to field laboratories whose

location can be optimised with transport theory and othertools, but must take into account population density,infrastructure, incidence, prevalence, equipment maintenance,

communications, and staff housing, to name but a few factors.Control programme optimisationis encountered further into theanalyses when programme response and evaluation areincluded.

Geographical informationsystem 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 disseminatinggeographically-referenced information (i.e. spatial data). Theincreasing importance of GIS is strongly correlated with the riseof the information age, particularly the development of powerful computing technology.

Spatial data is defined as geographical features and theattributes of these features. The features can be points, lines orpolygons, which can be used, for example, to represent thelocations of animals, rivers or forest patches, respectively. Eachindividual geographical feature can be linked to specificattribute information. Such attributes could be, for example,

the disease status and the age of each animal, the name of theriver, or the predominant tree species in the forest patch. Eachfeature will often have multiple attributes.

To manage spatial data, a GIS requires both spatial and non-spatial database management functionality. The geographicalfeatures are managed by the spatial data functions, which alsomaintain links to the attribute data. The latter are often storedin a standard database management system (DBMS). Thestrength of this linkage varies between different GIS softwarepackages, but a true GIS should allow spatial querying of theattribute database and thereby allow examination andmanagement of different attributes taking into account theproximity of spatial features. This would allow, for example, thecalculation of distances between infected animals, or estimationof the distances from diseased animals to the nearest river. Suchfunctionality can only be handled using geographicalco-ordinates in a standard DBMS. Fortunately, extensions toDBMS can correctly manipulate geographic co-ordinates as wellas the intersection, clipping or union of polygons and vectors.

Data collectionSpatial feature information can be imported into a GIS using anumber of different methods. Geographical co-ordinate

information can be manually digitised, read from paper maps,or determined directly in the field using global positioningsystems (GPS). An additional conversion step is required if images are generated as an intermediate step, such as when

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InputAcquisition

Data managementQuality control

Presentation/ CommunicationVisualisation

AnalysisVisualisationExplorationModellingVerification

Fig. 1Components of geographical information systems

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paper maps are scanned or landscapes are remotely sensedusing aeroplanes or satellites. However, the use of geo-referenced information which is already available in a digital

format is often possible.

Data quality is one of the principal concerns which every GISuser must deal with. Although this is also an issue for non-spatial data to be analysed, GIS has the additional requirementof spatially- and temporally-accurate recording of geographicalposition. Frequently, a spatially-accurate map of the differentvegetation types in a study area may be available, but the mapis not recent enough to represent the current situation. As aresult, the GIS user will often work with a spatial dataset that isthe result of combining geo-referenced information obtainedfrom different sources with potentially widely varying data

quality. In addition, the resolution of the digital map must beappropriate to the habitat of the home range, the age andseasonal activity patterns and the disease epidemiology. Forexample, the activity patterns of insectivorous bats andwoodland birds are very different even if their roosting sites areadjoining; some trematode habitats can be measured in metres,while others are best visualised in square kilometres.

Although population sampling of human or livestockpopulations which are readily accessible is a relatively simplestatistical exercise, this is not the case for wildlife. In simpleterms, the problem is to match animal availability with age,season, disease activity and statistical precision. For example,hunter-gathered samples, such as foxes, are usuallyheterogeneous because hunter-sampled areas are not randomlydistributed; hunters differ in enthusiasm to submit carcasses,leading to under- and over-representation; and whole familiesof foxes may be present in an area between February and June,when they live close together (in the case of Echinococcusmultilocularis, infection can be familial and thus familymembers are not independent units of any sample) (30).

Another problem with samples gathered by hunters is thatolder animals are under-represented. When catching (andreleasing) wild armadillos ( Dasypus novemcinctus), juveniles arereadily caught but are rarely infected with Mycobacteriumleprae. In southern Louisiana, 30% of adults can be infected,but only until significant numbers have been caught does thenon-clustering of the infection become clear (33, 25). Incontrast, if foxes are sought during July to September when thefamily groups have broken up, the E. multilocularisinfections in

juveniles will have maximised, thereby aiding statusrecognition and diagnosis (under endemic conditions, juvenilesare more often infected than adults). If only sporadic casesoccur, the rates for the two groups are similar.

Clearly, attempts must be made to develop a sampling schemewhich results in equal representation of all areas as far aspossible.

Data storageImportant differences exist between GIS software packages withrespect to the spatial data model used to store spatial featureinformation. Spatial data are usually divided into layersdescribing different types of information. For example, thedifferent locations in which a radio-tagged animal has beenrecorded during a study would be stored as one layer, and thevegetation types in the study area as another layer. The spatialdata represented by a particular layer can be stored using theraster or the vector model. In the raster model, space isrepresented as a regular grid in which the geographical spacecovered by an individual grid cell defines a spatial feature. Thismeans that within a layer, each of the different locations inwhich the aforementioned animal has been radio-tracked willbe represented by a rectangular grid cell, and the size of the grid

cells will therefore control the resolution and accuracy of thespatial data layer. This layer will then have a given number of grid cells depending on the resolution which was selected, andeach grid cell will store the frequency of the presence of theradio-tracked animal as values between zero and the maximumnumber of records within the space defined by an individualgrid cell. In contrast, the vector data model defines spatialfeatures, such as points, lines and polygons by a combinationof points linked by lines (arcs). In the case of a radio-trackedanimal, each location will be recorded as a point, and the layertherefore constitutes a series of x-y co-ordinates. This meansthat spatial data recorded using the vector model are inherently

more accurate compared to those recorded using the rastermodel. However, the raster model has the advantage that it ismethodologically easier to store data, to represent continuousphenomena such as rainfall and to perform spatial operationsbetween different data layers. The majority of GIS softwarepackages can now work with both data models, but the abilityto perform analytical spatial operations involving both rasterand vector layers simultaneously is limited.

Data analysisThe analysis of GIS data can be broadly categorised intovisualisation, exploration and modelling. Most GIS users will

conduct principally visual analyses. The type of mappresentation depends on the type of data available, either theactual event locations (such as the x-y co-ordinates of thelocation in which an animal was sighted), or aggregate data(where the number of animals within an area has beencounted).

Choropleth maps utilising administrative units with artificialboundaries are often used to present aggregate information. Inthis type of map, a shade or colour is assigned to theadministrative areas, thereby visualising the value of thevariable of interest. The hatching pattern or colour is based on

a class interval or continuous scale derived from a descriptivestatistic 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

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the spatial distribution of samples. Figures 2a and 2b describethe spatial distribution of the prevalence of tuberculosis in wildbadgers in Great Britain, which were examined as part of

badger removal operations between 1974 and 1997. The datais based on information recorded in the Central VeterinaryLaboratory Ecology (CVLE) Wildlife Unit Badger Database.The two maps represent two different methods of illustratingprevalence data. Compared to Figure 2a, Figure 2b has theadvantage of also incorporating information about the relativemagnitude of the total number of badgers examined, by scalingthe pie charts appropriately.

When inspecting choropleth maps, the following factorsshould be taken into consideration:

a) the boundaries of administrative or similar constructs areoften chosen for political or other reasons irrelevant to diseasespread, although such boundaries may have a direct impact onthe reporting of disease

b) sample size is frequently ignored when spatial data arepresented. Thus, while neighbouring areas may appear to havedifferent prevalence levels, the confidence limits may overlapand the differences are therefore more likely to be random

c)as a result of the above, mapping disease data in this mannermay lead to false interpretations of disease clusters or disease-free zones (22)

d) all relationships observed between variables may only holdin that particular aggregation of data; this is known as themodifiable areal unit problem with specific reference todifferences between natural and artificial area constructs andthe ecological fallacy in epidemiology of applying inferencesgained at a higher level to a lower level of aggregation (13, 16).

A range of methods has been developed to deal with theseproblems (6, 12, 27). These methods include Kriging, byinterpolating values between the centroids of each area (24);pycnophylactic interpolation, by iteratively interpolating acontinuous surface from data given by irregular geographicpolygons (32); use of a moving average filter (5); statisticallycontrolled successive unification of neighbouring units(STACSUNU) on the assumption that neighbouring unitswhose sampling results do not justify statistical distinction aremerged; 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 edgeeffects (21).


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County boundariesPrevalence of tuberculosis infection00-0.030.03-0.10.1-0.110.11-0.160.16-0.180.18-0.210.21-0.290.29-0.5No data

County boundaries

Positive for tuberculosis

Negative for tuberculosis

Fig. 2Prevalence of tuberculosis in wild badgers in Great Britain examined during badger removal operations over the period 1974-1997,aggregated by countyRadii of pie charts scaled according to the size of badger population examined; note that no pie charts are presented for counties without data

a) b)

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Point data may require additional manipulation to facilitatemeaningful visualisation. Figure 3a presents an example of asimple visualisation of point data based on a longitudinal study

of bovine tuberculosis in Australian brushtail possums(Trichosurus vulpecula) in New Zealand (26). The map showsthe capture locations of tuberculous wild possums and thelocations of all traps, draped over a 10 m contour map.Figure 3b presents a digital terrain model of the study area witha choropleth representing Thiessen polygons draped over it.The Thiessen polygons are used to indicate the areas whichhave been used by tuberculous possums and different coloursindicate which areas were used by possums infected with thefour different restriction endonuclease analysis (REA) typesidentified in the area. Visual examination of this map suggeststhat some degree of separation exists between the areas inwhich the different REA types occur.

With increased sophistication of computerised technology,three-dimensional map representations have become very easyto produce. Introduction of visual bias is particularly easy whencreating such maps, for example through the use of vertical

exaggeration factors, or choice of specific presentationperspective and azimuth. However, appropriate use of the thirddimension can substantially increase the visual impact during

presentation, and if combined with interactive viewing, canbecome an effective tool for visual thinking as well as visualcommunication. Figure 3c shows the density of traps per 40 m 2

in the study area as a third dimension which was generatedusing kernel smoothing from point locations of traps (2). Thisindicates that trapping intensity was heterogeneous across thestudy area. In this particular study, radio transmitters wereattached to the animals to locate den sites, and Figure 3d showssmoothed representations of the density of den sites based onall den sites and those which were used by tuberculouspossums. The two maps suggest that the area with highesttuberculosis density was not the same as the area with highestden density. This also could have been demonstrated bycalculating the ratio of both maps, but this would not haveshown the actual magnitude of the numerator anddenominator values. When examining Figure 3, it is importantto keep in mind the ways in which presentation can bemanipulated to emphasise particular aspects.


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c) Smoothed representation of trap density d) Visual comparison of smoothed den and tuberculosis den density

Fig. 3Different types of map representations using data from a longitudinal study of tuberculosis in a wild possum population in New Zealand

REA : restriction endonuclease analysis

Locations of tuberculosis capturesTrap locations10 m contour lines

0 100 200 300 400 m

REA type 10REA type 4bREA type 4REA type 4aDigital terrain model

0 100 200 300 400 500 m

b) Digital terrain model with Thiessen representation of locations (traps and dens)used by tuberculous possums with different REA types

a) Contour map with trap locations and trapsin which tuberculous possums were captured

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The estimation of home ranges of wild animals represents ausage of GIS for visual and descriptive analysis which is specificto wildlife research. Ecological research has resulted in the

development of a large number of home range estimators, andGIS can be used to specifically investigate the overlap betweenhome ranges of wild animals, which will be useful forinvestigating disease spread within populations that are closelymonitored. Figure 4 presents the 95% kernel estimates of homerange from the longitudinal study of four possums which wereinfected with M. bovis; this is draped over a vegetation mapgenerated from a classified satellite image (36). The map showsthe extent of the overlap of the home ranges, and at the sametime indicates that these possums were principally movingaround in habitat covered by manuka bush vegetation.


Vegetation typesUnclassifiedPineBeechShrublandPodocarpManukaPastureManuka/gorseManuka/pasture

Fig. 4Home ranges (95% kernel estimates) of four tuberculouspossums, draped over a classified vegetation map derived froma 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 inaddition, the confounding problems of hunting, contact rates,grid sizes, numbers of telemetry fixes, times of those fixes, andsubsequent analytical methods must be considered. Differentalgorithms produce different home ranges using the same data.These various points are demonstrated in Figures 5, 6 and 7from Staubach et al. (28). While problematic when designing asingle study, these differences are even more difficult to dealwith when comparing results of different published studies(17).

Fig. 5Illustration of the increase of home range size of a foxaccording to ageSource : Staubach et al ., 2000 (28)

Fig. 6A comparison of different techniques for the analysis of homerange using the same data set from an adult fox over a period ofone month (n = 154)Source : Staubachet al., 2000 (28)

S i z e o

f h o m e r a n g e

( k m

2 )

0.6

0.5

0.4

0.3

0.2

0.1

20 40 60 80 100 120 140 160 180 200Number of fixes

Fig. 7Effect of the number of radio fixes on the estimate of the homerange size of a sub-adult fox using the minimum convex polygonmethod (n = 257; number of bootstrap replications = 100)Source : Staubachet al., 2000 (28)

A unique feature of GIS technologies is the capability togenerate new geographic data layers which are based on

overlaying different thematic layers. The layers can be linkedthrough Boolean logic, weighted combinations or probabilisticrelationships (3).

Exploratory analysis involves statistical examination of the datafor the presence of any patterns. Such analysis is not used totest 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. Thesemethods produce either general statistics indicating that aspatial cluster exists within the area, or local statistics showingthe 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 agraphical presentation of the expected density of pointlocations depending on distance (1). With point data, the

a) 3 months (n = 65) b) 6 months (n = 174) c) 9 months (n = 216)

a) Minimum convexpolygon

b)Harmonic mean 95% c)Kernel densityestimator 95%probability isopleth

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spatial scan statistic (19) allows identification of the locationand the extent of spatial clusters (Figs 8a and 8b). In theexample 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 inspectionof Figure 3d.

Aggregated data, such as the prevalence of infected animals perarea construct, can be examined for the presence of spatialclustering using the Moran or Geary coefficients for spatialauto-correlation. The spatial scan statistic can also be used toobtain a statistic which will identify the location of likelyclusters (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 summarystatistics describing the habitat, particularly in terms of itsfragmentation and the mixture of vegetation types (15). Theparticular relevance of this methodology comes from the

realisation that the presence of wild animal species is likely tobe influenced by environmental factors which are presentwithin 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 theability of an animal species to survive in such an area (14). Thiswas pointed out in the context of fox rabies by Tinline, whosuggested that the survival of rabies virus in a fox populationmay require a certain mixture of forest/pasture habitats (31).Similarly, foxes infected by E. multilocularisare more frequentlyfound near water, in areas of high soil humidity, and onpastures, but under-represented in forest areas, suggesting thatdryness may limit the viability of E. multilocularisoncospheres(29). Such information about landscape fragmentation can then

be used as an additional predictor of likely disease presencewithin a regression modelling framework, but as with all othermodelled predictions, it only has value when verified bysubsequent field studies.

Spatial statistical disease modelling is aimed at investigatinghypothesised causal effects which are considered to beassociated with the occurrence of disease clusters. Such modelswill become useful for the management of wild animal diseaseif they have acceptable predictive accuracy. Myers et al. describethe use of risk mapping systems to forecast disease epidemicsaffecting humans (23). In the future, such systems could also bedeveloped to predict wildlife diseases. Regression algorithmswhich are sufficiently robust to deal with the dependencestructure of spatial data have recently been developed, but areonly available when using specialised computing tools.Bayesian random effects regression models can now be used tospatially model the relative risk of infection, incorporating anyimportant covariates (20, 35).

Geographical information systems have already become anessential tool for decision making, and the integration of multi-criteria and multi-objective decision analysis functions providesan additional set of tools (11). These methods allow thegeneration of decision rules incorporating both qualitative andquantitative information, and take into account the costs as wellas the benefits of these decisions. The weights of evidencemethod is a quantitative approach for combining evidence insupport of a hypothesis (3). Although developed for mineralpotential mapping, this method can also be used to spatiallypredict the probability of the presence of diseased animals givena weighted combination of predictor layers. In multi-criteriadecision-making, fuzzy criteria can also be used, or criteriauncertainty can be taken into consideration. This methodologycould be used, for example, when deciding where to placevaccine bait in the case of wild animal vaccination campaigns.


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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: primarycluster; yellow: secondary cluster; blue: secondary cluster)

Fig. 8Spatial cluster investigation of tuberculosis in wild possums

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Data presentation and disseminationOne of the particular strengths of GIS is the presentationfunctionality of the technology. Maps can be generated whichare tailored to specific requirements. These maps can representseveral types of attribute information in the same map, whichcan be two or three dimensional. Figure 3 shows a selection of such presentation methods. Digital video can be produced toillustrate the temporal dynamics of infection across a landscape.Interactive presentations can also be generated, which can thenbe made widely accessible via the Internet. However, it isimportant to bear in mind that map presentation, just as anyother type of graphical representation, with the help of inexpensive computing and ingenious techniques for imageprocessing, provides endless opportunities for mischief (34).

Geographical informationsystems in wildlife diseaseresearchThe majority of wildlife disease research involving the use of GIS is associated with diseases for which wildlife represent areservoir of infection for domestic animals or humans, such asbovine tuberculosis, West Nile virus and rabies. Wild badgersand brushtail possums are considered to be reservoirs of bovinetuberculosis in the United Kingdom and New Zealand,respectively. Delahay et al. made extensive use of GIS andspatial 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 conclusionthat infection foci remain very stable over time within specificsocial groups, and spread only very slowly to neighbouringgroups.

A predictive model of 80% accuracy was developed by Boone

to determine the serological presence of Sin Nombre virusinfection in deer mice ( Peromyscus maniculatus) in Walker RiverBasin, Nevada and California in the United States of America(USA) (4). The model was derived using multiple discriminantanalysis on the basis of remote sensing and GIS data.

The interaction between disease in wild and domestic animalsor humans is one of the areas where extensive use can be madeof GIS methodology and spatial analysis (7). In this context,quantification of the potential for direct or indirect contactbetween the two species is often necessary. While fairly accurateinformation may be available about the spatial presence of domestic animals, that same information will be much moreuncertain for wild animals, but GIS can provide the necessarytools to enable such analyses.

Geographical informationsystems in wildlife diseasesurveillance

Animal disease surveillance is aimed at monitoring endemic,epidemic, emerging and new diseases. A technically difficultand costly undertaking for domestic animals, such surveillanceis an even more challenging task for wild animals. In situationswhere animal species serve as reservoirs or vectors, surveillancemechanisms have existed for a long time. For example, mostcountries world-wide record the occurrence of wildlife rabies.Usually, this is only performed at some level of spatialaggregation, such as the district or national level. More effectivesystems record the actual locations in which cases have beenreported, and thereby allow more detailed investigations withrespect to spatio-temporal dynamics.

Wildlife disease surveillance for new and emerging diseases isalways likely to receive a low priority because of the costimplications associated with effective systems. It therefore mustbe accepted that such systems may have only limited sensitivityfor detecting such diseases, at least initially. In the case of WestNile virus, surveillance activities have the highest sensitivity forhuman and domestic animal cases, but very poor sensitivity forwild animal species such as wild birds. Similarly, areas distantfrom clinics and/or laboratories infrequently or never submitsamples; this has been a problem when determining the truestatus of coyote rabies in west Texas and the success of the oralvaccination programme.

Wildlife disease surveillance data generally suffer from a lack of denominator information. The data are also likely to be affectedby strong reporting bias, and therefore must be interpreted withgreat caution. Figure 9 presents an example of this problem;absence of reports of rabies in jackals from a particular countrydoes not necessarily mean absence of cases of rabies in jackals.


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Cases of rabies in jackals

Fig. 9Cases of rabies in jackals in Africa, parts of Europe and Asia, asreported to the World Health Organization in 1994(dotted areas did not report rabies in any animal species)

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At the sub-national level, geo-referenced data on rabiesoccurrence has been recorded by the Department of LivestockServices, Harare, Zimbabwe, for over ten years. This

information has been very useful for descriptive purposes, butparticularly in the case of wildlife data, the absence of denominator information and the unquantifiable potential forreporting-bias result in mapped presentations of case numbersbeing difficult to interpret; for an example, see Figure 10a.

Analyses can be conducted amongst diagnosed cases, as shownin Figure 10b, as long as reporting is not affected by differentialselection bias. In this particular map, the pie charts show thatcases of rabies in jackals represented the majority of rabiesdiagnoses in the north of the country, and at the same time thisis the location in which most animal rabies was reported.

The interpretation of wildlife disease surveillance can beenhanced by investigation of spatial patterns of occurrence.Curtis describes the use of information about the spatial

heterogeneity of laboratory submissions to identify countieswith inadequate reporting in the State of Kentucky, USA (8).The method is based on a proximity filter which works bycomparing the actual number of submissions in an area (suchas a county area or a circle) to the distribution of casessurrounding that area. A randomisation procedure is used tocompare the actual number of submissions against an expecteddistribution. 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 notreported properly by local officers).

ConclusionsGeographical information systems are admirably suited towildlife studies if only because these animals are mobile. Wildanimals do not live and die within arbitrarily fixed boundariesas 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 mapsand similar spatial visualisations, a process of visual thinking isencouraged, which allows the human brain to rapidly absorband interpret information and make further connections.However, this can also be abused through the type of presentation chosen (colours, three-dimensional) and bias inthe calculations and choice of data. Modern computingtechnology now allows GIS to work with very large,multidimensional datasets. Thus, GIS can use analyticalmethods that previously could not be imagined and canperform these methods rapidly and repeatedly. This allowsmental/intellectual exploration of the whole ecologicalsituation, but especially in space and time, which are the mostimportant 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 witheasy-to-use statistics software became available to all, so similarmisuses and inappropriate utilisations of GIS analyticalmethods are currently seen. Therefore, any appraisal of a GISreport on wildlife diseases must question whether the correctdata and analyses have been used.


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b) Proportion of cases in jackals (red) amongst all confirmed animal rabiesdiagnoses (green); pie charts scaled according to total number of rabiesdiagnoses

a) Laboratory-confirmed cases

00-56-1314-3637-82

No. of cases

Fig. 10Spatial distribution of cases of rabies in jackals in Zimbabwe

between 1991 and 1993, aggregated by administrative unitlevel 3Data provided by Dr J. Bingham, Department of Livestock Services,Harare, Zimbabwe

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Les systmes dinformation gographique appliqus lvaluationpidmiologique et la gestion des maladies de la faune sauvage

D.U. Pfeiffer & M. Hugh-Jones

RsumLes systmes dinformation gographique (SIG) permettent dintgrer lesanalyses spatiales dans les enqutes pidmiologiques sur les maladies de lafaune sauvage. Ces systmes comprennent une srie de modules permettant lasaisie, la gestion, lanalyse et la prsentation des donnes ; les SIG sont une technologie dintgration, cest--dire quils permettent de combiner desinformations provenant de sources extrmement varies et dcrivant diffrentsaspects de lenvironnement de la faune sauvage. La fonction analytique des SIG

est, encore aujourdhui, en constante volution, et utilise des mthodes visuelles,exploratoires ainsi que des techniques de modlisation. Les donnescartographiques gnres par les SIG offrent lavantage de permettre unereprsentation implicite, et perceptible intuitivement, des relationsdinterdpendance spatiale. La technologie devient une composante essentielledes systmes modernes dpidmiosurveillance.

Mots-cl sAnalyse spatiale pidmiologie Faune sauvage Systmes dinformationgographique.

Los sistemas de informaci n geogr fica como instrumento deevaluaci n epidemiol gica y gesti n de las enfermedades de lafauna salvaje

D.U. Pfeiffer & M. Hugh-Jones

Resumen

Los sistemas de informaci n geogr fica (SIG) facilitan la integraci n derelaciones espaciales en la investigaci n epidemiolgica sobre enfermedades dela fauna salvaje. El SIG, constituido por una serie de m dulos de entrada, gesti n,anlisis y presentaci n de los datos, es una tecnolog a integradora en la medidaen que permite combinar informaci n de origen muy diverso para describirdiferentes aspectos del medio en el que viven los animales salvajes. Lasprestaciones anal ticas de los SIG, cada vez m s perfeccionadas, abarcan desdelos m todos visuales a los de exploraci n y de elaboraci n de modelos. La salidacartogr fica que genera un SIG presenta la ventaja de poder reproducirimpl citamente, y de forma intuitiva, relaciones de dependencia espacial. La tecnolog a est en v as de convertirse en un componente b sico de los sistemasmodernos de vigilancia sanitaria.

Palabras claveAnlisis espacial Epidemiolog a Fauna salvaje Sistemas de informacin geogrfica.

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References1. 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 informationsystems for geoscientists: modelling with GIS. ElsevierScience, Oxford, 398 pp.

4. Boone J.D., McGwire K.C., Otteson E.W., DeBaca R.S., KuhnE.A., Villard P., Brussard P.F. & St. Jeor S.C. (2000). Remotesensing and geographic information systems: charting SinNombre 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 mappingusing the Kriging method: application to an influenza-likeillness epidemic in France. Am. J. Epidemiol., 135 (11),1293-1300.

7. Clifton-Hadley R.S. (1993). The use of a geographicalinformation system (GIS) in the control and epidemiology of bovine tuberculosis in south-west England. In Proc. AnnualMeeting of the Society for Veterinary Epidemiology andPreventive 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 forinhomogeneous populations. J. roy. Statist. Soc., B, 52 (1), 73-104.

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 high-density 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). Spatialstatistical methods in environmental epidemiology: a critique.Stat. Methods Med. Res., 4 , 137-159.

13. Fotheringham A.S. & Wong D.W.S. (1991). The modifiableareal unit problem in multivariate statistical analysis. Environ.Planning A, 23 , 1025-1044.

14. Gough M.C. & Rushton S.P. (2000). Application of GIS-modelling to mustelid landscape ecology. Mammal Rev.,30 (3/4), 197-216.

15. Gustafson E.J. (1998). Quantifying landscape spatialpattern: 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. Lytnen,eds). Taylor & Francis, London, 29-47.

17. Harris S., Cresswell W.J., Forde P.G., Trewhalla W.J., WoollardT. & Wray S. (1990). Home-range analysis using radio-tracking data a review of problems and techniquesparticularly as applied to the study of mammals. MammalRev., 20 (2/3), 97-123.

18. Kareiva P. (1994). Space: the final frontier for ecologicaltheory. Ecology, 75 (1), 1.

19. Kulldorff M. & Nagarwalla N. (1995). Spatial diseaseclusters: detection and inference. Stat. Med., 14 , 799-810.

20. Lawson A.B. (2001). Statistical methods in spatialepidemiology. John Wiley & Sons, Chichester, 277 pp.

21. Lawson A.B. & Williams F.L.R. (2001). An introductoryguide 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 preparednessin public health. In Remote sensing and geographicalinformation 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 lepraeinfection within a population of wildnine-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 thecommon brushtail possum. In Proc. 6th InternationalSymposium for Veterinary Epidemiology and Economics(S.W. Martin, ed.), 12-16 August, Ottawa. Department of Population Medicine, Ontario Veterinary College, Universityof Guelph, Ontario, Canada, 430-433.

27. Smans M. & Esteve J. (1992). Practical approaches todisease mapping. In Geographical and environmentalepidemiology: methods for small-area studies (P. Elliott,

J. Cuzick, D. English & R. Stern, eds). Oxford UniversityPress, Oxford, 141-150.

28. Staubach C., Stiebling U., Ziller M., Tackmann K., Thulke H.H.& Schlter H. (2000). The consequences of using differentanalysis techniques on wildlife study data to model diseasetransmission. In Proc. 9th Symposium of the InternationalSociety 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, FortCollins, 885-887.

8/13/2019 10.1.1.131.1586

12/12

29. Staubach C., Thulke H.-H., Tackmann K., Hugh-Jones M.E. &Conraths F.J. (2001). Geographic information system-aidedanalysis of factors potentially influencing the spatialdistribution of Echinococcus multilocularisinfections 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 Echinococcusmultilocularis infection in foxes. In World HealthOrganization/Office International des Epizooties Manual onechinococcosis in humans and animals: a public healthproblem of global concern (J. Eckert, M.A. Gemmell,F.-X. Meslin & Z.S. Pawlowski, eds). Office International desEpizooties, Paris, 182-188.

31. Tinline R.R. (1988). Persistence of rabies in wildlife. InRabies (J.B. Campbell & K.M. Charlton, eds). Kluwer

Academic Publishers, Boston, 301-322.

32. Tobler W.R. (1979). Smooth pycnophylactic interpolationfor geographic regions. J. Am. vet. med. Assoc., 74 , 519-535.

33. Truman R.W., Kumaresan J.A., McDonough C.M., Job C.K. &Hastings R.C. (1991). Seasonal and spatial trends in thedetectability of leprosy in wild armadillos. Epidemiol. Infect.,106 (3), 549-560.

34. Tufte E.R. (1997). Visual explanations images andquantities, evidence and narrative. Graphics Press, Cheshire,Connecticut, 156 pp.

35. Wakefield J.C., Best N.G. & Waller L. (2000). Bayesianapproaches 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, 104-127.

36. Worton B.J. (1989). Kernel methods for estimating theutilization distribution in home-range studies. Ecology, 70 (1),164-168.


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