Modelling bovine trypanosomosis spatial distribution by GIS in an agro pastoral zone of Burkina Faso
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Modelling bovine trypanosomosis spatial distribution by GIS in an agro pastoral zone of Burkina Faso

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UN CO RR EC TE D PR OO 3 Modelling bovine trypanosomosis spatial 4 distribution by GIS in an agro-pastoral 5 zone of Burkina Faso 6 Jean-Franc¸ois Michela,*, Stephane Drayb, Stephane de La Rocquea, 7 Marc Desquesnesa, Philippe Solanoc, Gerard De Wispelaered, 8 Dominique Cuisanceee 9 aCIRDES/CIRAD-EMVT, Bobo Dioulasso, Burkina Faso, France 10 bUniversite Claude Bernard Lyon I, Villeurbanne, France 11 cIRD/IPR, Bouake, Cote d'Ivoire, France 12 dCIRAD-EMVT, Maison de la teledetection, Montpellier, France 13 eCIRAD-EMVT, Campus International de Baillarguet, Montpellier, France 14 15 Abstract 16 17 Modelling of the spatial distribution of bovine trypanosomosis prevalence in Sideradougou district 18 Burkina Faso was performed by using a combination of spatial and statistical analysis. Based on a 19 comprehensive and geographically representative census of herds and farms in the area, more than 20 2000 cattle were randomly chosen and their blood sampled during field survey. Data on livestock 21 farming practices were recorded for each farm. All data were mapped within a GIS to generate new 22 information on spatial constraints in the area. 23 Surveys results were analysed and serological prevalence data were modelled using logistic 24 regression. The model allowed identification and quantification of risk factors. In a second step the 25 statistical model was used predictively on the entire farm population in the area.

  • modelling bovine

  • modelling disease spatial

  • trypanosomosis

  • eralized linear model

  • head

  • practised during

  • systems practised

  • function used


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3456789011121314151617181910212223242526272829203132333435312PreventiveVeterinaryMedicine1728(2002)1–14ModellingbovinetrypanosomosisspatialdistributionbyGISinanagro-pastoralzoneofBurkinaFasoJean-Franc¸oisMichela,*,St´ephaneDrayb,St´ephanedeLaRocquea,MarcDesquesnesa,PhilippeSolanoc,G´erardDeWispelaered,DominiqueCuisanceeeaCIbRUDnEivS/erCsIitR´eADCl-aEuMdeVTB,eBrnoabrodDLiyoounlaIs,sVoi,llBeuurrkbiannaneF,asFor,anFcreancecIRD/IPR,Bouake,CˆotedIvoire,FrancedeCIRACDI-REAMDV-TE,MCVaTm,pMusaiIsnotnerdneatliaontae´ll´eddeetBeactiilloanr,guMeot,ntMpeolnlitepre,llFirear,ncFeranceAbstractModellingofthespatialdistributionofbovinetrypanosomosisprevalenceinSideradougoudistrictBurkinaFasowasperformedbyusingacombinationofspatialandstatisticalanalysis.Basedonacomprehensiveandgeographicallyrepresentativecensusofherdsandfarmsinthearea,morethan2000cattlewererandomlychosenandtheirbloodsampledduringfieldsurvey.Dataonlivestockfarmingpracticeswererecordedforeachfarm.AlldataweremappedwithinaGIStogeneratenewinformationonspatialconstraintsinthearea.Surveysresultswereanalysedandserologicalprevalencedataweremodelledusinglogisticregression.Themodelallowedidentificationandquantificationofriskfactors.Inasecondstepthestatisticalmodelwasusedpredictivelyontheentirefarmpopulationinthearea.Thismethodwassuccessfulinpredictingtheserologicalprevalenceforeachindividualherdinthesample,fromtheirlivestockmanagementpatternsandspatiallocation.PredictedprevalenceswererepresentedwithintheGIS,takingdailymovementsofanimalsintoaccount.Spatialdistributionofprevalencewouldillustratespecificlocationsatriskfromanepidemiologicalviewpoint.Itgivesevidencethatthehydrologicalnetworkandlandoccupationpatternsinthesavanna-typecountrysideareplayinganimportantpartwhenstructuringaso-called‘‘trypanosomosisspace’’.#2002PublishedbyElsevierScienceB.V.Keywords:GIS;Spatialmodelling;Logisticregression;Trypanosomosis;Epidemiology*Correspondingauthor.E-mailaddress:jefmichel@wanadoo.fr(J.-F.Michel).0167-5877/02/$–seefrontmatter#2002PublishedbyElsevierScienceB.V.PII:S0167-5877(02)00120-4
63738393041424344454647484940515253545556575859506162636465666768696071727372J.-F.Micheletal./PreventiveVeterinaryMedicine1728(2002)1141.IntroductionAnimaltrypanosomosesareoneofthemainpathologicalconstraintsonthedevelopmentofanimalproductioninsub-SaharanAfrica(HurseyandSlingenbergh,1995),andcauseannuallossesestimatedatUS$1billion(DeHaanandBekure,1991).Tsetseiesarethemainvectors.Theriskoftransmissionisprimarilylinkedtotheintensityoftheencountersbetweenvectorsandhosts,anddependsonthespatialandtemporalinterfacesbetweentheprotagonistsinthepathogensystem(hostvectorparasite)(Laveissi`ereetal.,1986;DeLaRocqueetal.,1999).High-riskareashavebeenidentiedonthisbasisinanagro-pastoralzoneofsouthernBurkinaFaso,takingenvironmentalandsocio-economicfactorsintoaccount.Theavailabledataweregeoreferenced,includedintoageographicinformationsystem(GIS),andhigh-riskareaswereidentiedbyspatialmodelling(DeLaRocqueetal.,2001),asitwasperformedattheotherscales(Hendrickxetal.,2001).Theserologicalprevalenceofthedisease(prevalenceofantibodiesdirectedagainsttrypanosomalantigens)wasstudiedonasampleofcattlefarmsinthestudyarea,tovalidatethelistofepidemiologicalriskareasidentied.However,thedataobtainedwerebothpartialandspatiallydisjointed.Themethoddescribedherewassubsequentlydevelopedforestimatingandmodellingdiseasespatialdistribution,withaviewtomakingthedatacompatiblewiththelayersofgeographicdataavailableforthestudyzoneasawhole.2.Materialandmethods2.1.StudyzoneThestudywasconductedinpart(1200km2)oftheSid´eradougouagro-pastoralzonesouthofBobo-Dioulasso(BurkinaFaso),118Nand48W(Fig.1).Thezonehas10001100mmofrainfallperyear,withadryseasonfromNovembertoAprilandarainyseasonfromMaytoOctober.ItistypicaloftheSudaniantropicalclimatezone,withbushysavannasandforeststandsalongitswatercourses.Thesetypesofriversidevegetationarethepreferredbiotopesofthetsetseiesfoundinthezone,GlossinatachinoidesandGlossinapalpalisgambiensis(Challier,1973;Gruvel,1975).2.2.Population,samplinganddiagnosisThecattleinthezonewerecountedexhaustively,basedonthedwellingsbywhichtheyarepennedduringthenight(Micheletal.,1999).Foreachdwelling,thenumberofhead,theirwateringpointsattheendofthedryseason,andinformationontranshumancewererecorded.Thegeographicpositionsofeachdwellingandthewateringpointorpoints(twoatmost)weredeterminedbyglobalpositioningsystem(GPS)(GarminTM).Over800dwellings,with16,576head,werevisited.Theherdsweresplitintothreecategories:(i)smallunitswithoneortwopairsofdraughtoxen;(ii)mixedunits,generallywithfewerthan20head,includingdraughtoxenandafewbreeders;(iii)largeherdsofseveraldozenhead,withtranshumanceoftenpractisedduringthedryseason.Inthiszone,wherelivestockareamajorcomponentoffarmingsystemspractised,therearemanysmall
.giF.1coLnoitafoehtRocqueetal.,2001).sydut.enozhTeSide´radougousap-orgalarotenozsidetacolnihtehtuosfoossaluoiD-oboBanikruB(saF,)ota11N8nadW84retfa(DeaL
4757677787970818283848586878889809192939495969791013011100546017018014J.-F.Micheletal./PreventiveVeterinaryMedicine1728(2002)114Table1HerdsizeandheadnumberinthelocalpopulationHerdsizeUnderfivehead520headOver20headNNuummbbeerrooffahneirdmsals1347726((959%%))1816881((2141%%))13133473((1870%%))latoT16587061andmedium-sizedherds,whichaccountforover80%offarmsbutonly20%oftheanimals.Ontheotherhand,80%ofthecattleinthezoneareownedby17%ofthefarmers(Table1).Theherdsarefoundinthreemainzones(Fig.2):(i)anagriculturalzoneinwhichanimalproductioniscloselyintegratedintothefarmingsystem,withmedium-sizedherds,intheeast(zone1);(ii)amixedagriculturalandpastoralzoneinthewest,withsmallandlargeherds(zone2);(iii)analmostexclusivelypastoralzoneinthesouth,withlargeherds(zone3).Thisdistributioncorrespondstothepatternforcrops(DeLaRocqueetal.,2001).Inthewholestudyarea,thereareveryfewtradingandnon-tradingexchangesofcattle.Atwo-stagesamplingwasperformed.Therstsamplingunitwasonherd,i.e.ananimalmanagementunitsubjecttocommonanimalproductionpractices.Itwaseasilyidentiableintheeldandcorrespondstoanepidemiologicalentity.Theherdsweredrawnatrandom.Thesecondsamplingunitwasanimalswhichwerechosenasfollows:(i)exhaustivesamplinginsmallherds(fewerthanvehead);(ii)10headatmostinmedium-sizedherds(betweenveand20head);(iii)20headatmostinlargeherds(over20head).Withintheherds,theheadweredrawnatrandom,withoutreplacement.Forlogisticalreasonsitwasdecidedtosample2000headspreadover15%oftheherdsinthezone.Aquestionnaireonanimalproductionpracticeswaslledinforeachherd.Bloodsamplesweretakenfromthejugularvein.TheplasmawasanalysedinthelaboratoryusingthreeindirectELISAsystems(T.vivax,T.bruceiandT.congolense),revealingantibodiesagainstTrypanosomaspp.(Desquesnesetal.,2000).2.3.AvailabledataandstatisticalmodelSeveraltypesofdatawereusedtoanalyseandmodeltrypanosomosisseroprevalenceintheherds:Serologicaldatacorrespondingtothevariabletobeexplained.Animalhusbandrydataobtainedfromthefieldsurvey:herdsize,transhumancepracticesandthetypeofwateringpointusedattheendofthedryseason.SpatialdatageneratedbytheGISfromthegeographicpositionofthedifferentunits:distancebetweendwellingandwateringpoint,andproximityofdwellingstothehydrologicalnetwork.Thedescriptivevariableswereclassiedaccordingtoknowledgeofpractices(Lhosteetal.,1993),andtheirepidemiologicalsignicance(Table2).Thetypeofwateringpointwasdividedintospringsandrivers(whicharepropitioustotsetseies),andwellsand
901011111211311411511611711811911021121221J.-F.Micheletal./PreventiveVeterinaryMedicine1728(2002)1145Fig.2.Herds,sampledherdsandagriculturaldistributioninthestudyzone.Thesizeofthepointsvariesaccordingtothelogeofherdsize.Theherdsarefoundinthreemainzoneswhicharedelineatedbythehydrographicnetwork:(i)anagriculturalzoneinwhichanimalproductioniscloselyintegratedintothefarmingsystem,withmedium-sizedherds,intheeast;(ii)amixedagriculturalandpastoralzoneinthewest,withsmallandlargeherds;(iii)analmostexclusivelypastoralzoneinthesouth,withlargeherds.Thisdistributioncorrespondstothepatternforcrops.boreholes(whicharegenerallyfoundinzonesnotfavourabletotheies).Thezoneclassedasneighbouringonthehydrographicnetworkwassetat2km,basedonknowndataonthetsetseysabilitytospread(Cuisanceetal.,1985).Theserologicalprevalenceforeachherdwasmodelledusinglogisticregression,sincetheresponsevariableisaproportionandtheerrorfunctionisassumedtofollowthebinomiallaw(McCullaghandNelder,1989).Thelinkfunctionusedwasthelogitfunction,denedaslogitðpÞ¼logeðp=ð1pÞÞ.Anover-dispersionphenomenonoftenappearsinusinggen-eralizedlinearmodelwithalogitlinkwhentheresponsevariableisaproportion.Over-dispersionmeansthatthevarianceoftheresponsevariableexceedsthebinomialvarianceandthisproblemisverycommoninlarge-scaleepidemiologicalstudies(McCullaghandNelder,1989).Takingintoaccounttheover-dispersionproblem,weusedaquasi-likelihoodapproach(McCullaghandNelder,1989)intheplaceoflikelihoodfunction.Thisledtowidercondenceintervalofparametersthantheclassicalapproach.Forthesamereasons,totestthecontributionofthedifferentdescriptivevariablesinthemodel,weconducteda
3214215216217218219210311312313314315316317318319310411412413414415416417411844196J.-F.Micheletal./PreventiveVeterinaryMedicine1728(2002)114Table2DescriptivevariablesusedformodellingaCodeVariableLevelstypewTypeofwateringpointusedindryseasonArt:artificialNat:naturaldistwDistancebetweenfarmandwateringpointusedindryseason1:<1000m2:10004000m3:>4000m1:small(<5)2:medium(520)3:large(>20)oNseYoNseYhrdszHerdsizeHy2kmFarmlessthan2kmfromhydrographicnetworkallyrAnimalskeptbydwellingallyearroundaReferencelevelsforthemodelareshowninitalic.devianceanalysiswithF-testintheplaceofw2-test(Collet,1991).Thecoefcientsobtainedinthemodelwereinterpretedbycalculatingtheoddsratiosandtheircondenceinterval(Bouyeretal.,1995).Thisenabledustoquantifytheriskfactorsassociatedwiththelevelsofeachoftheexplanatoryvariablesinrelationtoareferencelevel.Asthemodelwasnotspatialized,itwasnecessarytolookforautocorrelationamongresiduals.Ifthepresenceofautocorrelationwasdetected,itcouldimplytheomissionofregressorvariables,thepresenceofnon-linearrelationshipsorthattheregressionmodelshouldhaveanautoregressivestructure(CliffandOrd,1973).Totesttheautocorrelation,werstlyestablishedneighbourhoodrelationshipsbetweenherdsusingaDelaunaytriangulationasproposedbySchmoyer(1994).Inthesecondstep,Geary(Geary,1954)andMorans(Moran,1948)statistics(seealsoCliffandOrd,1973)werecomputedforresiduals.Bypermutingthevaluesoftheresidualmap,wecomputednewvaluesofautocorrelationstatisticsandtheobservedvalueistestedbycomparingtothesetofvaluesobtainedforthepermutations.Asthenumberofpossiblepermutationwasverylarge,weusedaMonte-Carlo(Manly,1991)versionofthetest.Thesameprocedurehasbeencarriedoutforobservedandpredictedprevalencevalues.ThiskindofprocedurehasbeenrecentlyusedinKleinschmidtetal.(2000)withthenon-parametricD-statistic(Walter,1992)tomeasureautocorrelationofpredictionsfromalogisticregression.Whentheseindiceswereappliedtoobservedandpredictedprevalencevaluesandtheresidualsofthemodel,theyenabledustotestthecapacityofamodeltotakeaccountofthespatialnatureofdata.Thestatisticalmodelwastheninvertedtoestimatetheserologicalprevalenceforalltheherdsinthezone,usingtheexplanatoryvariablessharedwiththesurvey,whichwerethesamethanthoseusedtogeneratethemodel.AllcalculationsweremadeusingtheRsoftware(IhakaandGentleman,1996).2.4.SpatialmodelSpecicproblemslinkedtothegeographicalnatureofmappedobjectssuchasherdsandthe‘‘inherd’’variabilityofmeasuredvariableshavetobeconsideredwhenmapping
051151251351451551651751851951061161261361461561661761861961071171271371471571671771871971081181281381481581681781881819091J.-F.Micheletal./PreventiveVeterinaryMedicine1728(2002)1147seroprevalence:(i)herdsarepointsthatmaybesuperimposediftheyareclosetooneanother,hencemaskinginformation;(ii)themeaningofprevalencewithinaherdvarieswiththenumberofheadintheherd,aprevalenceof50%inatwoheadherdhasnotthesamesignicanceasaprevalenceof50%ina100headherd;(iii)mappingonlythepointscorrespondingtothepensusedatnightprovidesonlyapartialrepresentationofrealityasanimalsmoveandoccupyacontinuousspace;(iv)spatialinformationonprevalencehastobecompatiblewiththeotherinformationavailableintheGISiftheyaretobecompared.Toovercometheseproblems,aspatialmodeloflandoccupationbycattleandofdiseasedistributionwasdeveloped.AllspatialobjectmanipulationsusedtheMapinfoTMsoft-.erawTherepresentationoflandoccupationbycattleinazoneasawholeisbasedonmodellingthedailymovementsoftheanimalsineachherd.Insavannazones,wateravailabilityisthemainconstraintattheendofthedryseason,andgovernsmovements(Boutrais,1994).Herdmovementswerethereforemodelledbyrepresentingthedirectroutebetweenthenightpenandthewateringpointorpoints,anddrawingabufferzonearoundtheroute,correspondingtotheareaoccupiedbythecattleduringtheday(Micheletal.,1999).Thiszoneofdailyusebytheherdvariesinsize.Thewidertheherdandtheneareritistoitswateringpoint,thelargerthezoneoffrequentation(Fig.3).Thismodelwasvalidatedbymonitoringthemovementsofasampleofherds.Thepredictedprevalenceforalltheherdswasappliedtotheirzonesofdailyuse.Tosynthesizethisinformation,whichwasnotyetveryeasytoresolveduetothesuper-impositionofpolygons,itprovednecessarytoaggregateitsoastoshifttoasmallerscale.Thiswasdonebyprojectingallthezonesofuseandthecorrespondingprevalencesontoaregulargeographicgridof1km2(Raynaletal.,1996).Thecumulateddistributionofantibody-prevalenceinthestudyzonewasthenrepresentedbyassigningtoeachsquarethemeanvalueoftheprevalencesfortheherdpolygonsimpingingonit,soastoproduceamapofaverageprevalence(Fig.4).Thecalculatedmeanvalueofprevalencewasweightedbythesizeofherdsinordertotakeintoaccountforproblems(ii)citedabove.Smoothingbytwo-dimensionalweightedlocalregression(ClevelandandDevlin,1988)onthecentroidsofthesquaresinthegridmadethemapsmorerealistic.3.Results3.1.SamplingandobservedseroprevalenceIntotal,216herdsand1784headweresampled.Herdandcattledistributioninthesampleshowedthatsmallherdswereslightlyunder-represented,infavourofmedium-sizedherds(Table3).Ontheotherhand,smallherdswereover-representednearthehydrographicnetwork(Fig.2).Thedifferencesinrelationtothesampleinitiallyplannedcanbeattributedtoeldconstraintssuchasherdsinanout-of-the-wayplaceorcattlebreederabsentornotinagreementwithtakingabloodsample.Theaverageserologicalprevalenceobservedamongthecattlewas73.4%.Themapofherdprevalence,shownaspointsaccordingtothecorrespondingdwelling,showedcasedistributionbutwasdifculttointerpret(Fig.5).
8J.-F.Micheletal./PreventiveVeterinaryMedicine1728(2002)114Fig.3.Modellingofdailyherdmovements.Modellingherdmovementsconsistsinrepresentingthedirectroutebetweenthenightpenandthewateringpointorpoints,anddrawingabufferzonearoundtheroute,correspondingtotheareaoccupiedbythecattleduringtheday.Thiszoneofdailyusebytheherdvariesinsize.Thelargertheherdandtheneareritistoitswateringpoint,thelargerthezoneoffrequentationis(BV:cattle)(afterDeLaRocqueetal.,2001).Table3HerdsizeandheadnumberinthesampleHerdsizeUnderfiveheadNumberofherds110(51%)Numberofanimals327(18%)520head70(32%)736(41%)Over20head36(17%)721(41%)latoT1271864
191291391491591691791891991002J.-F.Micheletal./PreventiveVeterinaryMedicine1728(2002)1149Fig.4.Dataaggregationandspatialdistributionofmeanprevalence.Thiswasdonebyprojectingallthezonesofuseandthecorrespondingprevalencesontoaregulargeographicgridof1km2.Thecumulateddistributionofantibody-prevalenceinthestudyzonewasthenrepresentedbyassigningtoeachsquarethemeanvalueoftheprevalencesfortheherdpolygonsimpingingonit,soastoproduceamapofaverageprevalence.3.2.Statisticalmodelling:identificationofriskfactorsThedevianceanalysisshowedthatonlythedistancebetweencattlepenandwateringpointwasnotsignicantandthisvariablewasexcludedfromthemodel.Alltheothervariablesweresignicant(Table4).Thedispersionparameterforthemodelwas2.48.Therelationbetweenthenumbersofobservedandpredictedpositives(Spearmansrankcorrelationr¼0:45,P<0:0001)showedthatthestatisticalmodelhasagoodt.Thespatialautocorrelationtestsrevealedapositivecorrelationbetweenobservedandpredictedprevalences,whereastheresidualsofthemodelwerenotcorrelated(Table5).Thevariablesusedinthemodelthustakeaccountofthespatialfactor.TheoddsratioscalculatedwiththecoefcientsestimatedbythemodelshowedthatproximitytotheTable4DevianceanalysisofthemodelDegreeofDevianceResidualdegreeResidualP(>F)freedomoffreedomdevianceNULL––215666.51typew117.66214648.86<0.0082hrdsz237.37212611.49<0.0006allyr159.07211552.41<0.0001hy2km154.17210498.24<0.0001
10222030240250260270201J.-F.Micheletal./PreventiveVeterinaryMedicine1728(2002)114Fig.5.Distributionofserologicalprevalenceamongtheherdssampled.Themapofseroprevalencefortheherdssampled,shownaspointsaccordingtothecorrespondingdwelling,indicatedcasedistribution.BV:cattle.hydrographicnetwork,frequentationofnaturalwateringpoints,largeherdsizeandthefactofkeepinganimalsneardwellingsallyearroundwereallriskfactors(Table6).3.3.ModellingofspatialdistributionofprevalenceThemapofpredictedserologicalprevalences,obtainedbyspatialmodellingonawholestudyzonescale,showedthatmeanserologicalprevalenceisdistributedalongthehydrographicnetwork,withfocalpointsofhighvalues,andthatitspreadsradiallyintotheneighbouringsavannas(Fig.6).Table5Spatialautocorrelationtestsfortheobservedandthepredictedprevalencesandtheresiduesofthemodel,usingMorans(I)andGearys(c)indexesVariableIObservedprevalence0.196Predictedprevalence0.542Residualsofmodel0.020P-values<0.001<0.001842.0c997.0564.00279.P-values<0.001<0.001882.0
802902012112212312412J.-F.Micheletal./PreventiveVeterinaryMedicine1728(2002)11411Table6Oddsratios(OR)calculatedfromthecoefficientsoftheserologicalmodelVariableLowerconfidenceORinterval(OR)Intercept0.531.08typewnat0.881.34hrdsz20.380.67hrdsz31.031.99allyryes1.692.70hy2kmyes1.943.43Upperconfidenceinterval(OR)91.240.261.148.364..3017Fig.6.Distributionofmeanserologicalprevalenceinthezoneandhigh-transmission-riskzones.Thehigh-transmissionriskzonesaredelineatedbyawhiteoutline.Thismapshowsthatmeanserologicalprevalenceisstructuredlinearlyalongthehydrographicnetwork,withfocalpointsofhighvalues,andthatitspreadsradiallyintotheneighbouringsavannas.4.Discussion4.1.ObservedprevalenceandstatisticalmodelTheserologicalresultsobtainedfromthesampleconrmtheenzooticsituationthathadalreadybeenobservedfortrypanosomosisintheSide´radougouzone,withhighinfectionlevelsamongvectors(DeLaRocque,1997).Onananimalproductionzonescale,parasitepressurecanbeevaluatedmoreaccuratelybythenumberofantibodycarriersthanbydirectdetectionofparasites(Desquesnesetal.,2000).Withserologicaldata,thestatisticalmodel
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