Quasi-likelihood techniques in a logistic regression equation for identifying Simulium damnosum s.l.larval habitats intra-cluster covariates in Togo  

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 

机构地区：[1]Global Infectious Disease Research Program,Department of Public Health,College of Public Health,University of South Florida,3720 Spectrum Blvd,Suite 304,Tampa,FL 33612,USA [2]Onchocerciasis Control Programme(OCP),B.P.1473 Avenue Norte,Zombre,Sector 8,Ouagadougou,Burkina Faso,West Africa [3]Department of Chemistry,University of Pittsburg,3260 Forbes Avenue,First Floor,USA [4]Department of Biology,Wake Forest University,226 Winston Hall,Box 7325 Reynolda Station,Winston-Salem,NC 27109,USA [5]School of Economic and Policy Sciences,The University of Texas at Dallas,800 West Campbell Road,Richardson,TX 75080-3021,USA

出　　处：《Geo-Spatial Information Science》2012年第2期117-133,共17页地球空间信息科学学报（英文）

基　　金：This work was produced by the US National Institute of Health/Fogarty International Center under SR01TW008508.

摘　　要：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 effects.Generally,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 residuals.Unfortunately,inconspicuous errors in residual intracluster correlation estimates can overstate precision in forecasted S.damnosum s.l.riverine larval habitat explanatory attributes regardless how they are treated(e.g.independent,autoregressive,Toeplitz,etc.).In this research,the geographical locations for multiple riverine-based S.damnosum s.l.larval ecosystem habitats sampled from two preestablished epidemiological sites in Togo were identified and recorded from July 2009 to June 2010.Initially,the data were aggregated into PROC GENMOD.An agglomerative hierarchical residual cluster-based analysis was then performed.The 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(i.e.QuickBird 0.61m wavbands).Orthogonal spatial filter eigenvectors were then generated in SAS/Geographic Information Systems(GIS).Univariate and nonlinear regression-based models(i.e.logistic,Poisson,and negative binomial)were also employed to determine probability distributions and to identify statistically significant parameter estimators from the sampled data.Thereafter,Durbin–Watson statisti

关 键 词：Simulium damnosum s.l.cluster covariates QuickBird Onchoceriasis Annual biting rates Bayesian TOGO 

分 类 号：O17[理学—数学]