Hypo-endemic onchocerciasis hotspots: defining areas of high risk through micro-mapping and environmental delineation

Background:Onchocerciasis(river blindness)caused by the parasite Onchocercavolvulus and transmitted by riverine Simulium spp.(Black flies)is targeted for elimination in Africa.This is a significant change in strategy from the‘control’of meso-and hyper-endemic areas through mass drug administration(MDA)with Mectizan®(ivermectin),to the‘elimination’in all endemic areas where a range of interventions may be required.The most significant challenges of elimination in low transmission or hypo-endemic areas are two-fold.First,there are vast remote areas where the focality of low transmission is relatively undefined.Second,the treatment with ivermectin increases the risk of serious adverse events(SAEs)in individuals with high parasitaemias of Loa loa,a filarial parasite widespread in Central and West Africa,which causes Tropical eye worm and transmitted by Chrysops spp.(Deer flies).Discussion:We therefore propose novel mapping approaches using remote sensing satellite and modelled environmental data to be used in combination with rapid field surveys to help resolve the problems of targeting the expansion of onchocerciasis elimination activities in L.loa co-endemic areas.First,we demonstrate that micro-stratification overlap mapping(MOM)of available onchocerciasis and loiasis prevalence maps can be used to identify 12 key high risk areas,where low O.volvulusand high L.loa transmission overlap,which we define as“hypo-endemic hotspots”.Second we show that integrated micro-mapping of prevalence data,and the use of environmental data to delineate riverine and forest risk factors associated with Simulium spp.and Chrysops spp.vector habitats can further help to define target intervention areas i.e.secondary hotspots within hotspots,to help avoid the risk of SAEs.Summary:These mapping examples demonstrate the value of bringing prevalence,entomological and ecological information together to develop maps for planned implementation and targeted strategies.This is critical as better mapping may the reduce costs and lower the L.loa associated risks,especially if there are extensive areas of low endemicity that may require treatment with ivermectin or alternative strategies.Novel cost-effective approaches are necessary if elimination of O.volvulus transmission in Africa is to be achieved in an efficient and safe way by the goal of 2025.

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

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 statistics were used to test the null hypothesis that the regression residuals were not autocorrelated against the alternative that the residuals followed an autoregressive process in AUTOREG.Bayesian uncertainty matrices were also constructed employing normal priors for each of the sampled estimators in PROC MCMC.The residuals revealed both spatially structured and unstructured error effects in the high and low ABR-stratified clusters.The analyses also revealed that the estimators,levels of turbidity,and presence of rocks were statistically significant for the high-ABR-stratified clusters,while the estimators distance between habitats and floating vegetation were important for the low-ABR-stratified cluster.Varying and constant coefficient regression models,ABRstratified GIS-generated clusters,sub-meter resolution satellite imagery,a robust residual intra-cluster diagnostic test,MBR-based histograms,eigendecomposition spatial filter algorithms,and Bayesian matrices can enable accurate autoregressive estimation of latent uncertainity affects and other residual error probabilities(i.e.heteroskedasticity)for testing correlations between georeferenced S.damnosum s.l.riverine larval habitat estimators.The asymptotic distribution of the resulting residual adjusted intra-cluster predictor error autocovariate coefficients can thereafter be established while estimates of the asymptotic variance can lead to the construction of approximate confidence intervals for accurately targeting productive S.damnosum s.l.habitats based on spatiotemporal field-sampled count data.

Elimination of onchocerciasis in Africa by 2025:the need for a broad perspective

Background:In response to the recent publication Is onchocerciasis elimination in Africa feasible by 2025:a perspective based on lessons learnt from the African control programmes"by Dadzie et al.,it is important to clarify and highlight the positive and unequivocal research and operational contributions from the American experience towards the worldwide elimination of human onchocerciasis(river blindness).Main text:The strategies of twice or more rounds of mass drug administration(MDA)of ivermectin per year,as well as the use of OV-16 serology have allowed four American countries to be verified by World Health Organization to have eliminated transmission of Onchocerca volvulus,the etiological agent.These advances were also implemented in Sudan and Uganda;currently,both are the only African countries where ivermectin MDA was safely stopped in several transmission zones.Conclusions:Programmatic treatment and evaluation approaches,pioneered in the Americas,are the most efficient among the existing tools for elimination,and their broader use could catalyze the successful elimination of this disease in Africa.