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SPATIALAGENT-BASED SIMULATIONMODELING INPUBLICHEALTH
Design,Implementation,andApplications forMalariaEpidemiology
S.M.NIAZARIFIN
DepartmentofComputerScienceandEngineering UniversityofNotreDame IN,USA
GREGORYR.MADEY
DepartmentofComputerScienceandEngineering UniversityofNotreDame IN,USA
FRANKH.COLLINS
DepartmentofBiologicalSciences UniversityofNotreDame IN,USA
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ArifinS.M.Niaz,author.
Spatialagent-basedsimulationmodelinginpublichealth:design,implementation,andapplicationsfor malariaepidemiology/S.M.NiazArifinGregoryR.Madey,FrankH.Collins. p.;cm.
Includesbibliographicalreferencesandindex. ISBN978-1-118-96435-4(hardback)
I.Madey,GregoryRichard,author.II.Collins,FrankH.,author.III.Title. [DNLM:1.Malaria–epidemiology.2.ComputerSimulation.3.GeographicInformationSystems.4.Models, Theoretical.5.SpatialAnalysis.WC755.1]
RA644.M2
614.5′ 32090285–dc23
2015033121 PrintedintheUnitedStatesofAmerica
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B.Sc.Engg.(Civil),FIE(B),PGD(CS) MyFatherandGuide
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andmywife: RumanaReazArifin
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Ph.D.,M.S.,B.S. WeGrewupTogether
—S.M.NiazArifin
1Introduction 1
1.1Overview,1
1.2Malaria,3
1.3Agent-BasedModelingofMalaria,4
1.4Contributions,4
1.5Organization,5
2Malaria:ABriefHistory 7
2.1Overview,7
2.2MalariainHumanHistory,7
2.2.1TheMalarialPath:AncientOrigins,8
2.2.2NamingandKeyDiscoveries,9
2.2.3AntimalarialDrugs,9
2.2.4PreventionMeasures,10
2.3MalariaEpidemiology:AGlobalView,10
2.3.1TheMalariaParasite,11
2.3.2GeographicDistribution,12
2.3.3TypesofTransmission,12
2.3.4RiskMappingandForecasting,13
2.4MalariaControl,13
3Agent-BasedModelingandMalaria17
3.1Overview,17
3.2Agent-BasedModels(ABMs),17
3.2.1Agents,18
3.2.2Environment,19
3.2.3Rules,20
3.2.4SoftwareforABMs,20
3.3HistoryandApplications,21
3.3.1M&SOrganizations,21
3.4AdvantagesofABMs,23
3.4.1Emergence,Aggregation,andComplexity,23
3.4.2Heterogeneity,24
3.4.3LearningandAdaptation,24
3.4.4FlexibilityinSystemDescription,24
3.4.5InclusionofMultipleSpaces,25
3.4.6LimitationsofABMs,25
3.4.7ABMsvsMathematicalModels,27
3.4.8ApplicabilityofABMsforMalariaModeling,28
3.5MalariaModels:AReview,29
3.5.1MathematicalModelsofMalaria,30
3.5.2Agent-BasedModels(ABMs)ofMalaria,33
3.5.3The Spatial DimensionofMalariaModels,35
3.6Summary,36
4TheBiologicalCoreModel39
4.1Overview,39
4.1.1RelevantTermsofInterest,40
4.2TheAquaticPhase,41
4.2.1Egg(E),42
4.2.2Larva(L),43
4.2.3Pupa(P),45
4.3TheAdultPhase,46
4.3.1ImmatureAdult(IA),46
4.3.2MateSeeking(MS),47
4.3.3BloodMealSeeking(BMS),47
4.3.4BloodMealDigesting(BMD),47
4.3.5Gravid(G),47
4.4AquaticHabitatsandOviposition,48
4.4.1AquaticHabitats,48
4.4.2Oviposition,48
4.5SenescenceandMortalityRates,50
4.5.1Senescence,50
4.5.2MortalityModels:BasicMathematicalFormulation,51
4.6MortalityintheCoreModel,51
4.6.1Aquatic(Immature)MortalityRates,52
4.6.2AdultMortalityRates,53
4.7Discussion,53
4.7.1AnExtendibleFrameworkforOtherAnophelineSpecies,53
4.7.2Weather,Seasonality,andOtherFactors,54
4.7.3MortalityRates,54
4.8Summary,54
5TheAgent-BasedModel(ABM)57
5.1Overview,57
5.2ModelArchitecture,58
5.2.1Object-OrientedProgramming(OOP)Terminology,58
5.2.2Agents,60
5.2.3Environments,62
5.2.4Event-Action-ListDiagram,62
5.3MosquitoPopulationDynamics,64
5.4ModelFeatures,66
5.4.1ProcessingStepsOrdering,66
5.4.2ModelAssumptions,67
5.4.3Simulations,69
5.5Summary,69
6TheSpatialABM71
6.1Overview,71
6.2TheSpatialABM,74
6.2.1DefinitioofTerms,74
6.2.2Landscapes,75
6.2.3LandscapeGeneratorTools,76
6.3ResourceClustering,79
6.4FlightHeuristicsforMosquitoAgents,81
6.5SimulationResults,85
6.5.1ModelVerification85
6.5.2LandscapePatterns,86
x Contents
6.5.3RelativeSizesofResources,87
6.5.4ResourceDensity,88
6.5.5CombinedHabitatCapacity(CHC ),89
6.6SpatialHeterogeneity,90
6.7Summary,93
7VerificationValidation,Replication,andReproducibility95
7.1Overview,95
7.2VerificatioandValidation(V&V):AReview,96
7.2.1AcceptabilityAssessmentandQualityAssurance(QA),96
7.2.2VerificatioandValidation(V&V),98
7.3ReplicationandReproducibility(R&R):AReview,100
7.4Summary,101
8VerificatioandValidation(V&V)ofABMs103
8.1Overview,103
8.2VerificatioandValidation(V&V)ofABMs,103
8.3Phase-WiseDocking,105
8.3.1AssumptionsfortheABMs,105
8.3.2Phase-WiseDockingResults,107
8.4CompartmentalDocking,110
8.4.1ImplementationsoftheABMs,111
8.4.2AssumptionsfortheABMs,112
8.4.3CompartmentalDockingResults,114
8.5Summary,116
9ReplicationandReproducibility(R&R)ofABMs121
9.1Overview,121
9.1.1SimulationStochasticity,122
9.1.2BoundaryTypes,123
9.2VectorControlInterventions,124
9.2.1LarvalSourceManagement(LSM),125
9.2.2Insecticide-TreatedNets(ITNs),126
9.2.3PopulationProfileforITNs,127
9.2.4CoverageSchemesforITNs,127
9.2.5ApplyingLSMinIsolation,130
9.2.6ApplyingITNsinIsolation,132
9.2.7ApplyingLSMandITNsinCombination,132
9.3SimulationResults,134
9.3.1SimulationStochasticity,134
9.3.2LSMinIsolation,134
9.3.3ImpactofBoundaryTypes,137
9.3.4ITNsinIsolation,138
9.3.5LSMandITNsinCombination,143
9.4ReplicationandReproducibility(R&R)Guidelines,147
9.5Discussion,150
9.6Summary,152
10ALandscapeEpidemiologyModelingFramework
10.1Overview,155
10.2GISinPublicHealth,159
10.3TheStudyAreaandtheABM,160
10.3.1FeaturesoftheSpatialABM,161
10.3.2VectorControlInterventionScenarios,162
10.4TheGeographicInformationSystem(GIS),163
10.4.1TheGIS-ABMWorkflw,163
10.4.2GISProcessingofDataLayers,164
10.4.3FeatureCounts,165
10.5SimulationsandSpatialAnalyses,165
10.5.1OutputIndices,166
10.5.2HotSpotAnalysis,167
10.5.3KrigingAnalysis,167
10.6Results,168
10.6.1MosquitoAbundance,168
10.6.2OvipositionCountperAquaticHabitat,171
10.6.3BloodMealCountperHouse,174
10.7Discussion,177
10.7.1StochasticityandInitialConditions,177
10.7.2ModelCalibrationandParameterization,178
10.7.3Emergence,178
10.7.4Complexity,179
10.7.5DataResolution(Granularity),179
10.7.6SpatialAnalyses,180
10.7.7Habitat-basedInterventions,181
10.7.8MiscellaneousIssues,181
10.8Conclusions,182
11TheEMODIndividual-BasedModel
PhilipA.EckhoffandEdwardA.Wenger
11.1Overview,185
11.1.1Motivation:ModelingofMalariaEradication,186
11.1.2QuestionsthatAriseintheContextofMalaria Eradication,187
11.1.3SpatialHeterogeneityandMetapopulationEffects,188
11.1.4ImplicationsforModelStructure,190
11.2ModelStructure,193
11.2.1HumanDemographicsandSyntheticPopulation,193
11.2.2VectorEcology,194
11.2.3VectorTransmission,195
11.2.4Within-HostDiseaseDynamics,197
11.2.5HumanMigrationandSpatialEffects,198
11.2.6StochasticEnsembles,200
11.3Results,201
11.3.1Single-VillageSimulations,201
11.3.2SpatialSimulations:GarkiDistrict,202
11.3.3Madagascar,203
11.4Discussion,206
AppendixAEnzymeKineticsModelforVectorGrowthand Development209
A.1Overview,209
A.2StochasticThermodynamicModels,210
A.3PoikilothermicDevelopmentModels,210
A.3.1Log-LinearModels,211
A.3.2TheArrheniusModel,211
A.3.3TheEyringEquation,212
A.3.4TheGibbsFreeEnergy,Entropy,andEnthalpy,212
A.3.5IncorporatingEntropyandEnthalpyintoEyringEquation,213
A.4TheSharpeandDeMicheleModel,214
A.4.1EnergyStates,215
A.4.2ExponentialDistributionofTransitionTimes,215
A.4.3ProbabilityCalculations,215
A.5TheSchoolfiel etal. Model,217
A.6Depinay etal. Model,219
A.6.1CumulativeDevelopment,219
A.6.2Results,220
A.7Summary,221
AppendixBFlowchartfortheABM223
B.1FlowchartfortheAgent-BasedModel(ABM),223
AppendixCAdditionalFilesforChapter10233
AppendixDAPostsimulationAnalysisModuleforAgent-BasedModels239
D.1Overview,239
D.2SimulationOutputAnalysis:AReview,240
D.2.1StatisticalAnalysis,240
D.2.2VisualizationandAnalysisTools,242
D.3TheLiNKModel,243
D.3.1Agents,Interface,andPathogens,244
D.3.2SpaceandTime,244
D.3.3VerificatioandValidation,244
D.4P-SAMArchitecture,245
D.4.1TheWriter,245
D.4.2TheReader,246
D.4.3AdvantagesofusingPerl,246
D.5PostsimulationAnalysisandVisualization,247
D.5.1InfectionStatistics,247
D.5.2RoamingInfectionStatistics,247
D.5.3BirthandDeathStatistics,248
D.5.4PathogenTransmissionGraphs,248
D.5.5SummaryStatistics,249
D.6P-SAMPerformance,250
D.6.1Profiling252
D.6.2CodeOptimization,253
D.7Conclusion,254 References255 Index279
PREFACE
Intoday’sscientifiworld, computationalscience isconsideredthe thirdpillar of scientifiinquiry,alongwiththetwotraditionalpillarsoftheoryandexperimentation. Althoughscienceisstillcarriedoutasanongoinginterplaybetweentheoryandexperimentation,theincreasedscaleandcomplexityofbothhavecompelledcomputational sciencetobeanintegralaspectofalmosteverytypeofscientifiresearch.
Typically,computationalscienceusescomputersimulations(toconstructcomputationalmodels)andquantitativeanalysistechniquesinordertoanalyzeandsolve scientifiproblems.Inparticular, modeling&simulation (M&S)techniquesarebeing increasinglyusedtomodelcomplexsystems,whichingeneralexhibitcomplex propertiessuchasheterogeneity,dynamicinteractions,emergence,learning,and adaptation.Withtheever-wideningavailabilityofcomputingresources,theincreasing poolofhumancomputationalexpertsandduetoitsunconstrainedapplicabilityacross academicdisciplineboundaries,theimportanceofM&Scontinuestogrowata remarkablerate.
Agent-basedmodelingandsimulation (ABMS)isaclassofM&Stechniquesfor simulatingtheactionsandinteractionsofautonomousagentswithaviewtoassessing theireffectsonthesimulatedsystemasawhole.Havingitsrootsfromtheinvestigation ofcomplexsystems,complexadaptivesystems,artificiaintelligence,andcomputer science,ABMScombineselementsofgametheory,complexsystems,emergence, computationalsociology,multiagentsystems,andevolutionaryprogramming.The suiteofmodelsdevelopedusingABMS,knownas agent-basedmodels (ABMs),have applicationsindiversereal-worldproblemsandhavebecomeincreasinglypopularas amodelingapproachinalmostallbranchesofscienceandengineering.
Inpublichealthresearch,epidemicsandinfectiousdiseasedynamicsmodeling canbetermedasa signaturesuccess ofABMS.UsesofM&Sinpublichealth includesynthesizingknowledgefromdisparatedisciplines,fillinthegapsinexisting
knowledge,conductingcost-benefitrade-offstudies,andgeneratinghypotheses. Assuch,anincreasingnumberofU.S.universitiesareincorporatingsystemsscience andM&Sintotheircurriculaandresearchprogramsthroughtheschoolsofpublic healthandotherhealth-relatedacademicdepartments.
Amajorobjectiveofthisbookistopresentapracticalandusefulintroductiontothe importantfacetsofasufficientlcomplexM&SprojectthatlargelyinvolvedtheevolutionofacomplexABM.TheABMwasdevelopedbyexpertsfrommultipleacademic disciplines.Thus,majorportionsofthecontentsofthisbookmaterializedasaresult ofinterdisciplinary,collaborativeresearcheffortsconcerningABMS(fromComputer ScienceandEngineering)andmalariaepidemiology(fromBiologicalSciences)atthe UniversityofNotreDame[547].
Malariaisoneoftheoldestanddeadliestinfectiousdiseasesinhumans,andthecontrolofmalariarepresentsoneofthegreatestpublichealthchallengesofthetwenty-firs century.Accordingtothelatestestimates(releasedinDecember2014),theWorld HealthOrganization(WHO)reportedabout198millioncasesofmalariain2013and anestimated584,000deaths,withhalfoftheworld’spopulation(about3.3billion) beingatrisk[567].Humanmalariaistransmittedonlybyfemalemosquitoesofthe genus Anopheles,whichareregardedastheprimaryvectorsfortransmission.
TheABMspresentedinthisbookweredevelopedbyfollowingaconceptual,biologicalcoremodelof Anophelesgambiae (An.gambiae forshort)formalariaepidemiology.Thenotionofthiscoremodelplaysacentralroleinthelongdevelopmentprocess ofmultipleversionsoftheABMs,aswellasinconductingsuchcrucialstepsasmodel verificationvalidation,andreplication.Evolutionofthecoremodelhasbeenguidedby relevantbiologicalfeaturesconcerning An.gambiae,whichwereiterativelyrefineand incrementallyaddedtotheexistingpoolofmodelfeatures.Subsequently,theABMs wereupdatedtoreflecthechanges.
OUTLINEOFCHAPTERS
Chapter1ofthisbookintroducesthereadertoitsmajorcomponents,presentsabrief introductiontomalariaandABMs,andlistsourspecificontributions.Chapters2and 3presentgeneralintroductionstomalariaandABMs.Theirpurposeistocollectively serveasaconcisebackgroundforreaderswhoarelessfamiliarwiththediseaseandits epidemiologicalaspects,andwhyABMsareparticularlyusefulinmodelingdiseases likemalaria.
Chapter4thoroughlydescribesthebiologicalcoremodelof An.gambiae.After defininsomerelevanttermsofinterest,itaddressesseveralimportantfeaturesof themosquitolifecycle,includingdevelopmentindifferentlife-cyclestages,aquatic habitats,oviposition,vectorsenescence,anddensity-andage-dependentmortality rates.Italsodiscussessomeofthekeyfeatures,characteristics,andlimitationsofthe coremodel.
Chapter5discussesthedesignandimplementationofasimplifiedfiedversion oftheABM.SincetheABMisdevelopedinthe Java object-orientedprogramming
(OOP)language,wepresentsomerelevantOOPterminology.Wethendescribethe architectureoftheABMandpresentclassdiagramstoelaboratetheagentsandtheir environments.Inordertocapturethemajordailyeventsofatypicalsimulationinastandardfashion,anewtypeofdescriptivediagram,calledthe
Event–Action–List (EAL) diagram,ispresented.Thechapteralsodescribesthemosquitopopulationdynamics andsomeoftheothercharacteristicsandfeaturesoftheABM,includingprocessing stepsordering,initialization,andsimulationassumptions.
Chapter6presentsaspatialextensionoftheABM.Ingeneral,anABMcanbe appliedtoadomain with or without anexplicitrepresentationof space.However,analysisofspatialrelationshipsisfundamentaltoepidemiologyresearch,asdemonstratedby severalrecentstudies.Insomecases,anexplicitspatialrepresentationmaybedesired forcertainaspectsoftheABMtobemodeledmorerealistically.Forexample,ina malariaABM,somefrequenteventsperformedbythemosquitoagentssuchasobtainingasuccessfulbloodmeal(host-seeking)orfindinanaquatichabitattolayeggs (oviposition)canbe spatially modeledinthelandscapeinwhichtheagentsmove. Theseaspectsarealsoaffectedbytheunderlyingspatialheterogeneity,whichdefine thespatialdistributionofresourcesanddirectlyaffectsthemosquitopopulationinthe ABM.InChapter6,wedescribethemodelingaspectsofthespatialABM,themosquito agentsandtheirspatialmovement,thelandscapes,andtheresource-seekingevents. Wealsodescribeacustom-builtlandscapegeneratortoolthatisusedtogeneratelandscapeswithdesiredcharacteristicsforthespatialABMandpresentresultsconcerning theeffectsofvaryinglandscapepatterns,therelativesizeanddensityoftheaquatic habitats,theoverallcapacityofthesystem,andtheeffectsofspatialheterogeneityof thelandscapes.
Chapters7–9describethetechniquesandresultsofverificationvalidation,and replicationoftheABMs,whichingeneraldealwiththemeasurementandassessment ofaccuracyofM&Sresearch.Theyalsopresenttheresultsofexaminingtheimpact oftwomalariacontrolinterventions,namely,larvalsourcemanagement(LSM)and insecticide-treatednets(ITNs).WeinvestigatetheeffectsofLSMandITNs,applied bothinisolationandincombination,onthemosquitoagentpopulations.Wecompare ourresultstothosereportedbypreviouslypublishedmalariamodelsandrecommend guidelinesforfutureABMmodelers,summarizingtheinsightsandexperiencesgained fromourworkofreplicatingearlierstudies.
Chapter10presentsalandscapeepidemiologymodelingframeworkthatintegrates aGeographicinformationsystem(GIS)withthespatialABM.Theideaofintegrating GISwithABMsisnotnew,andseveralstudiesinmultipledomains(e.g.,urban land-usechange,militarymobilecommunications)haveshownsuchintegration. GISandspatialstatisticalmethodshavealsobeenextensivelyusedinentomological andepidemiologicalstudies.Inparticular,formalariaasadisease,GISapplications havebeenusedformeasuringthedistributionofmosquitospecies,theirhabitats, thecontrolandmanagementofthedisease,andsoon.However,withtheexception oftheindividual-basedmodelnamed EMOD (whichispresentedinChapter11), no ABM-basedmalariastudy hasyetshownhowtoeffectivelyintegrateanABM withGISandothergeospatialfeaturesandtherebyharnessthefullpowerofGIS.
Thereisalsoa vacuumofknowledge inbuildingrobustintegrationframeworksthat canguidetheuseofgeospatialfeatures(relatedtomalariatransmission)asmodel inputs,asopposedtosimplyusethesefeaturesascartographicoutputsfromthe models(asdonebymostpreviousstudies).InChapter10,weshowhowtoeffectively integratesimulationoutputsfromourspatialABMwithaGIS.Forastudyarea inKenya,weconstructdifferentlandscapescenariosandperformspatialanalyses onthesimulationresults.Resultsindicatethattheintegrationofepidemiological simulation-basedoutputswithspatialanalysestechniqueswithinasinglemodeling frameworkcanbeavaluabletoolforconductingavarietyofdiseasecontrolactivities suchasexploringnewbiologicalinsights,monitoringthechangesofkeydisease transmissionindicesandepidemiologicallandscapes,andguidingresourceallocation forfurtherinvestigation.
Lastly,Chapter11presentstheadvancedindividual-basedmodelnamed EMOD, whichiscontributedasaguestchapterfromtheInstituteforDiseaseModeling (IDM)[536].EMOD,whichstandsfor EpidemiologicalModeling,representsa suiteofdetailed,geographicallyspecificandmechanisticstochasticsimulations ofdisease(includingmalaria)transmissionthroughtheuseofcomplexsoftware modeling.Chapter11showcasestwoimportantepidemiologicalscenariosinAfrica withgeospatialmapscoupledwiththemodel’soutputs.
Attheend,weconcludewithafullyfunctionalcomputersourcecodeofaspecifi versionofthespatialABMispresentedintheBookCompanionSiteandasoftware modulecalledP-SAM(Post-SimulationAnalysisModule)thatwedevelopedtoanalyzeandvisualizethepostsimulationoutputsofABMs.
INTENDEDAUDIENCE
Thisbookisintendedforstudents,individuals,andresearchgroupswhointendto learnandusetheproblem-solvingmethodologyofM&S,particularlyusingtheABMS techniques.Itcanserveasapracticalresourceforstudentswithascienceorengineeringbackgroundattheseniorundergraduateorgraduatelevelandotherprofessionals interested,ingeneral,insimulationmodeling,epidemiology,publichealth,andbioinformatics.AlthoughsomefamiliaritywiththebasicnotionsofM&S,biology,and/or epidemiologymaybehelpful,noadvancedbackgroundinthesedisciplinesisnecessary.Mostofthecorematerialsareaccompaniedbyintroductorydetailstoimportant topics,definitionofrelevantterms,andcopiousreferences.WeuseJava™[264]as ourprogrammingenvironmentofchoiceindevelopingthespatialsimulationmodels. Areasonablelevelofcomputerprogrammingskillsishelpful,butnotmandatory,to comprehendtheresultsanddiscussionspresentedinChapters6,8,and9.
Ontheonehand,M&Sresearchers(includingstudentsandmodelers)canbenefi fromthebook’sdescriptionofthecoreconceptualmodel(Chapter4)followedbythe implementationdetailsoftheABMs(Chapter5),theextensionofthenonspatialABM intoaspatialABM(Chapter6),andthemodelverificationvalidation,andreplication issues(Chapters7–9).Thetransformationofmentalimagesofaconceptualmodel
(whichoftenresidesamorphouslyonlyinmodelers’brainsandmayvastlydifferamong individualmodelersduetocountlessambiguities)intoacomputational,verifiable entity(anABM)mayhelpnewmodelerstocomprehendtheoverallmodelinglifecycle.
Ontheotherhand,thisbookcanalsoproveusefultoawiderangeofotherindividualsfromintellectualsandacademicstoprofessionals.Duetothemultidisciplinary natureofthereportedresearchthatspansseveralacademicdisciplinesincluding, ABMS,bioinformatics,malariaepidemiology,spatialmodels,andGIS,itcanhave broadimplicationsandcanbevaluabletoinfectiousdiseasedynamicsresearchers, malariacontrolmanagers(e.g.,fromministriesofhealthofmalaria-endemiccountries),andotherpublichealthpolicymakersandfundingbodies.Forexample,sections describingtheimpactofmalariacontrolinterventions(inChapters9and10)can providevaluablebiologicalinsightstomalariamodelers,aswellastopolicymakers andfundingagenciesconcerningthedisease’scontrolandeliminationefforts.
Thelasttwochaptersareespeciallyrelevantforspecifiusergroups.Thelandscape epidemiologymodelingframeworkpresentedinChapter10,whichintegratesaGIS withthespatialABM(describedinChapter6),showcasesanidealmethodological frameworkandausefulapplicationoftheABMsbytakingthe virtual, simulated worldofagentsonestepclosertothe real, malarious worldofmosquitoes.Chapter11, throughtheuseofanotheradvancedindividual-basedmodel,showshowknowledge fromdiversebutinterconnecteddisciplinessuchasM&S,epidemiology,andGIS canbemeaningfullycombinedtoderiveinsightsandanalyzetheimplicationsfor malariaeradication.
S.M.NiazArifin
NotreDame,Indiana
June,2015
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