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Quasi-likelihood techniques in a logistic regression equation for identifying Simulium damnosum *** habitats intra-cluster covariates in Togo

引用

Geo-Spatial Information Science 2012年第2期15卷 117-133页

作者： Benjamin G.JACOB Robert J.NOVAK Laurent TOE Moussa S.SANFO Abena N.AFRIYIE Mohammed A.IBRAHIM Daniel A.GRIFFITH Thomas R.UNNASCH Global Infectious Disease Research Program Department of Public HealthCollege of Public HealthUniversity of South Florida3720 Spectrum BlvdSuite 304TampaFL 33612USA Onchocerciasis Control Programme(OCP) B.P.1473 Avenue NorteZombreSector 8OuagadougouBurkina FasoWest Africa Department of Chemistry University of Pittsburg3260 Forbes AvenueFirst FloorUSA Department of Biology Wake Forest University226 Winston HallBox 7325 Reynolda StationWinston-SalemNC 27109USA School of Economic and Policy Sciences The University of Texas at Dallas800 West Campbell RoadRichardsonTX 75080-3021USA

The standard methods for regression analyses of clustered riverine larval habitat data of Simulium damnosum s.l.a major black-fly vector of onchoceriasis,postulate models relating observational ecological-sampled parameter estimators to prolific habitats without accounting for residual intra-cluster error correlation ***,this correlation comes from two sources:(1)the design of the random effects and their assumed covariance from the multiple levels within the regression model and(2)the correlation structure of the ***,inconspicuous errors in residual intracluster correlation estimates can overstate precision in forecasted *** *** larval habitat explanatory attributes regardless how they are treated(***,autoregressive,Toeplitz,etc.).In this research,the geographical locations for multiple riverine-based *** *** ecosystem habitats sampled from two preestablished epidemiological sites in Togo were identified and recorded from July 2009 to June ***,the data were aggregated into PROC *** agglomerative hierarchical residual cluster-based analysis was then *** sampled clustered study site data was then analyzed for statistical correlations using monthly biting rates(MBR).Euclidean distance measurements and terrain-related geomorphological statistics were then generated in ArcGIS.A digital overlay was then performed also in ArcGIS using the georeferenced ground coordinates of high and low density clusters stratified by annual biting rates(ABR).The data was overlain onto multitemporal sub-meter pixel resolution satellite data(*** 0.61m wavbands).Orthogonal spatial filter eigenvectors were then generated in SAS/Geographic Information Systems(GIS).Univariate and nonlinear regression-based models(***,Poisson,and negative binomial)were also employed to determine probability distributions and to identify statistically significant parameter estimators from the

关键词： Simulium damnosum s.l.cluster covariates QuickBird onchoceriasis Annual biting rates Bayesian Togo

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