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 intra-cluster 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 2 pre-established epidemiological sites in Togo were identified and recorded from July 2009 to June 2010. Initially the data was 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). This 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/GIS. Univariate and non-linear 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 test 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, ABR- stratified 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.

Developing GIS-based eastern equine encephalitis vector-host models in Tuskegee, Alabama.

BACKGROUND: A site near Tuskegee, Alabama was examined for vector-host activities of eastern equine encephalomyelitis virus (EEEV). Land cover maps of the study site were created in ArcInfo 9.2 from QuickBird data encompassing visible and near-infrared (NIR) band information (0.45 to 0.72 microm) acquired July 15, 2008. Georeferenced mosquito and bird sampling sites, and their associated land cover attributes from the study site, were overlaid onto the satellite data. SAS 9.1.4 was used to explore univariate statistics and to generate regression models using the field and remote-sampled mosquito and bird data. Regression models indicated that Culex erracticus and Northern Cardinals were the most abundant mosquito and bird species, respectively. Spatial linear prediction models were then generated in Geostatistical Analyst Extension of ArcGIS 9.2. Additionally, a model of the study site was generated, based on a Digital Elevation Model (DEM), using ArcScene extension of ArcGIS 9.2. RESULTS: For total mosquito count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 5.041 km, nugget of 6.325 km, lag size of 7.076 km, and range of 31.43 km, using 12 lags. For total adult Cx. erracticus count, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 5.764 km, nugget of 6.114 km, lag size of 7.472 km, and range of 32.62 km, using 12 lags. For the total bird count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 4.998 km, nugget of 5.413 km, lag size of 7.549 km and range of 35.27 km, using 12 lags. For the Northern Cardinal count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 6.387 km, nugget of 5.935 km, lag size of 8.549 km and a range of 41.38 km, using 12 lags. Results of the DEM analyses indicated a statistically significant inverse linear relationship between total sampled mosquito data and elevation (R2 = -.4262; p < .0001), with a standard deviation (SD) of 10.46, and total sampled bird data and elevation (R2 = -.5111; p < .0001), with a SD of 22.97. DEM statistics also indicated a significant inverse linear relationship between total sampled Cx. erracticus data and elevation (R2 = -.4711; p < .0001), with a SD of 11.16, and the total sampled Northern Cardinal data and elevation (R2 = -.5831; p < .0001), SD of 11.42. CONCLUSION: These data demonstrate that GIS/remote sensing models and spatial statistics can capture space-varying functional relationships between field-sampled mosquito and bird parameters for determining risk for EEEV transmission.

A heteroskedastic error covariance matrix estimator using a first-order conditional autoregressive Markov simulation for deriving asympotical efficient estimates from ecological sampled Anopheles arabiensis aquatic habitat covariates.

BACKGROUND: Autoregressive regression coefficients for Anopheles arabiensis aquatic habitat models are usually assessed using global error techniques and are reported as error covariance matrices. A global statistic, however, will summarize error estimates from multiple habitat locations. This makes it difficult to identify where there are clusters of An. arabiensis aquatic habitats of acceptable prediction. It is therefore useful to conduct some form of spatial error analysis to detect clusters of An. arabiensis aquatic habitats based on uncertainty residuals from individual sampled habitats. In this research, a method of error estimation for spatial simulation models was demonstrated using autocorrelation indices and eigenfunction spatial filters to distinguish among the effects of parameter uncertainty on a stochastic simulation of ecological sampled Anopheles aquatic habitat covariates. A test for diagnostic checking error residuals in an An. arabiensis aquatic habitat model may enable intervention efforts targeting productive habitats clusters, based on larval/pupal productivity, by using the asymptotic distribution of parameter estimates from a residual autocovariance matrix. The models considered in this research extends a normal regression analysis previously considered in the literature. METHODS: Field and remote-sampled data were collected during July 2006 to December 2007 in Karima rice-village complex in Mwea, Kenya. SAS 9.1.4 was used to explore univariate statistics, correlations, distributions, and to generate global autocorrelation statistics from the ecological sampled datasets. A local autocorrelation index was also generated using spatial covariance parameters (i.e., Moran's Indices) in a SAS/GIS database. The Moran's statistic was decomposed into orthogonal and uncorrelated synthetic map pattern components using a Poisson model with a gamma-distributed mean (i.e. negative binomial regression). The eigenfunction values from the spatial configuration matrices were then used to define expectations for prior distributions using a Markov chain Monte Carlo (MCMC) algorithm. A set of posterior means were defined in WinBUGS 1.4.3. After the model had converged, samples from the conditional distributions were used to summarize the posterior distribution of the parameters. Thereafter, a spatial residual trend analyses was used to evaluate variance uncertainty propagation in the model using an autocovariance error matrix. RESULTS: By specifying coefficient estimates in a Bayesian framework, the covariate number of tillers was found to be a significant predictor, positively associated with An. arabiensis aquatic habitats. The spatial filter models accounted for approximately 19% redundant locational information in the ecological sampled An. arabiensis aquatic habitat data. In the residual error estimation model there was significant positive autocorrelation (i.e., clustering of habitats in geographic space) based on log-transformed larval/pupal data and the sampled covariate depth of habitat. CONCLUSION: An autocorrelation error covariance matrix and a spatial filter analyses can prioritize mosquito control strategies by providing a computationally attractive and feasible description of variance uncertainty estimates for correctly identifying clusters of prolific An. arabiensis aquatic habitats based on larval/pupal productivity.

Spatial distribution, blood feeding pattern, and role of Anopheles funestus complex in malaria transmission in central Kenya.

Studies were conducted to determine the role of sibling species of Anopheles funestus complex in malaria transmission in three agro-ecosystems in central Kenya. Mosquitoes were sampled indoors and outdoors, and rDNA PCR was successfully used to identify 340 specimens. Anopheles parensis (91.8%), A. funestus (6.8%), and Anopheles leesoni (1.5%) were the three sibling species identified. A. parensis was the dominant species at all study sites, while 22 of 23 A. funestus were collected in the non-irrigated study site. None of the 362 specimens tested was positive for Plasmodium falciparum circumsporozoite proteins by enzyme-linked immunosorbent assay. The most common blood-meal sources (mixed blood meals included) for A. parensis were goat (54.0%), human (47.6%), and bovine (39.7%), while the few A. funestus s.s. samples had fed mostly on humans. The human blood index (HBI) for A. parensis (mixed blood meals included) in the non-irrigated agro-ecosystem was 0.93 and significantly higher than 0.33 in planned rice agro-ecosystem. The few samples of A. funestus s.s. and A. funestus s.l. also showed a trend of higher HBI in the non-irrigated agro-ecosystem. We conclude that agricultural practices have significant influence on distribution and blood feeding behavior of A. funestus complex. Although none of the species was implicated with malaria transmission, these results may partly explain why non-irrigated agro-ecosystems are associated with higher risk of malaria transmission by this species compared to irrigated agro-ecosystems.

Effect of rice cultivation on malaria transmission in central Kenya.

A 12-month field study was conducted between April 2004 and March 2005 to determine the association between irrigated rice cultivation and malaria transmission in Mwea, Kenya. Adult mosquitoes were collected indoors twice per month in three villages representing non-irrigated, planned, and unplanned rice agro-ecosystems and screened for blood meal sources and Plasmodium falciparum circumsporozoite proteins. Anopheles arabiensis Patton and An. funestus Giles comprised 98.0% and 1.9%, respectively, of the 39,609 female anophelines collected. Other species including An. pharoensis Theobald, An. maculipalpis Giles, An. pretoriensis Theobald, An. coustani Laveran, and An. rufipes Gough comprised the remaining 0.1%. The density of An. arabiensis was highest in the planned rice village and lowest in the non-irrigated village and that of An. funestus was significantly higher in the non-irrigated village than in irrigated ones. The human blood index (HBI) for An. arabiensis was significantly higher in the non-irrigated village compared with irrigated villages. For An. funestus, the HBI for each village differed significantly from the others, being highest in the non-irrigated village and lowest in the planned rice village. The sporozoite rate and annual entomologic inoculation rate (EIR) for An. arabiensis was 1.1% and 3.0 infective bites per person, respectively with no significant difference among villages. Sporozoite positive An. funestus were detected only in planned rice and non-irrigated villages. Overall, 3.0% of An. funestus samples tested positive for Plasmodium falciparum sporozoites. The annual EIR of 2.21 for this species in the non-irrigated village was significantly higher than 0.08 for the planned rice village. We conclude that at least in Mwea Kenya, irrigated rice cultivation may reduce the risk of malaria transmission by An. funestus but has no effect on malaria transmission by An. arabiensis. The zoophilic tendency of malaria vectors in irrigated areas accounts partly for low malaria transmission rates despite the presence of higher vector densities, highlighting the potential of zooprophylaxis in malaria control.

Host choice and multiple blood feeding behaviour of malaria vectors and other anophelines in Mwea rice scheme, Kenya.

BACKGROUND: Studies were conducted between April 2004 and February 2006 to determine the blood-feeding pattern of Anopheles mosquitoes in Mwea Kenya. METHODS: Samples were collected indoors by pyrethrum spay catch and outdoors by Centers for Disease Control light traps and processed for blood meal analysis by an Enzyme-linked Immunosorbent Assay. RESULTS: A total of 3,333 blood-fed Anopheles mosquitoes representing four Anopheles species were collected and 2,796 of the samples were assayed, with Anopheles arabiensis comprising 76.2% (n = 2,542) followed in decreasing order by Anopheles coustani 8.9% (n = 297), Anopheles pharoensis 8.2% (n = 272) and Anopheles funestus 6.7% (n = 222). All mosquito species had a high preference for bovine (range 56.3-71.4%) over human (range 1.1-23.9%) or goat (0.1-2.2%) blood meals. Some individuals from all the four species were found to contain mixed blood meals. The bovine blood index (BBI) for An. arabiensis was significantly higher for populations collected indoors (71.8%), than populations collected outdoors (41.3%), but the human blood index (HBI) did not differ significantly between the two populations. In contrast, BBI for indoor collected An. funestus (51.4%) was significantly lower than for outdoor collected populations (78.0%) and the HBI was significantly higher indoors (28.7%) than outdoors (2.4%). Anthropophily of An. funestus was lowest within the rice scheme, moderate in unplanned rice agro-ecosystem, and highest within the non-irrigated agro-ecosystem. Anthropophily of An. arabiensis was significantly higher in the non-irrigated agro-ecosystem than in the other agro-ecosystems. CONCLUSION: These findings suggest that rice cultivation has an effect on host choice by Anopheles mosquitoes. The study further indicate that zooprophylaxis may be a potential strategy for malaria control, but there is need to assess how domestic animals may influence arboviruses epidemiology before adapting the strategy.

Hydrological modeling of geophysical parameters of arboviral and protozoan disease vectors in Internally Displaced People camps in Gulu, Uganda.

BACKGROUND: The aim of this study was to determine if remotely sensed data and Digital Elevation Model (DEM) can test relationships between Culex quinquefasciatus and Anopheles gambiae s.l. larval habitats and environmental parameters within Internally Displaced People (IDP) campgrounds in Gulu, Uganda. A total of 65 georeferenced aquatic habitats in various IDP camps were studied to compare the larval abundance of Cx. quinquefasciatus and An. gambiae s.l. The aquatic habitat dataset were overlaid onto Land Use Land Cover (LULC) maps retrieved from Landsat imagery with 150 m x 150 m grid cells stratified by levels of drainage. The LULC change was estimated over a period of 14 years. Poisson regression analyses and Moran's I statistics were used to model relationships between larval abundance and environmental predictors. Individual larval habitat data were further evaluated in terms of their covariations with spatial autocorrelation by regressing them on candidate spatial filter eigenvectors. Multispectral QuickBird imagery classification and DEM-based GIS methods were generated to evaluate stream flow direction and accumulation for identification of immature Cx. quinquefasciatus and An. gambiae s.l. and abundance. RESULTS: The main LULC change in urban Gulu IDP camps was non-urban to urban, which included about 71.5 % of the land cover. The regression models indicate that counts of An. gambiae s.l. larvae were associated with shade while Cx. quinquefasciatus were associated with floating vegetation. Moran's I and the General G statistics for mosquito density by species and instars, identified significant clusters of high densities of Anopheles; larvae, however, Culex are not consistently clustered. A stepwise negative binomial regression decomposed the immature An. gambiae s.l. data into empirical orthogonal bases. The data suggest the presence of roughly 11% to 28 % redundant information in the larval count samples. The DEM suggest a positive correlation for Culex (0.24) while for Anopheles there was a negative correlation (-0.23) for a local model distance to stream. CONCLUSION: These data demonstrate that optical remote sensing; geostatistics and DEMs can be used to identify parameters associated with Culex and Anopheles aquatic habitats.

Diversity of riceland mosquitoes and factors affecting their occurrence and distribution in Mwea, Kenya.

Knowledge of mosquito species diversity, occurrence, and distribution is an essential component of vector ecology and a guiding principle to formulation and implementation of integrated vector management programs. A 12-month entomological survey was conducted to determine the diversity of riceland mosquitoes and factors affecting their occurrence and distribution at 3 sites targeted for malaria vector control in Mwea, Kenya. Adult mosquitoes were sampled indoors by pyrethrum spray catch and outdoors by the Centers for Disease Control and Prevention light traps. Mosquitoes were then morphologically identified to species using taxonomic keys. The characteristics of houses sampled for indoor resting mosquitoes, including number of people sleeping in each house the night preceding collection, presence of bed nets, location of the house, size of eaves, wall type, presence of cattle and distance of the house to the cowshed, and proximity to larval habitats, were recorded. Of the 191,378 mosquitoes collected, 95% were identified morphologically to species and comprised 25 species from 5 genera. Common species included Anopheles arabiensis (53.5%), Culex quinquefasciatus (35.5%), An. pharoensis (4.7%), An. coustani (2.5%), and An. funestus (1.6%). Shannon's species diversity and evenness indices did not differ significantly among the 3 study sites. There was a marked house-to-house variation in the average number of mosquitoes captured. The number of people sleeping in the house the night preceding collection, size of eaves, distance to the cowshed, and the nearest larval habitat were significant predictors of occurrence of either or both An. arabiensis and Cx. quinquefasciatus. The peak abundance of An. arabiensis coincided with land preparation and the first few weeks after transplanting of rice seedlings, and that of Cx. quinquefasciatus coincided with land preparation, late stage of rice development, and short rains. After transplanting of rice seedlings, the populations of Cx. quinquefasciatus were collected more outdoors than indoors, suggesting a shift from endophily to exophily. These results demonstrate that irrigated rice cultivation has a strong impact on mosquito species occurrence, distribution, abundance, and behavior, and that certain house characteristics increase the degree of human-vector contact.

Environmental factors associated with the distribution of Anopheles arabiensis and Culex quinquefasciatus in a rice agro-ecosystem in Mwea, Kenya.

Studies were conducted between May and June, 2006 to investigate the environmental factors affecting the distribution of An. arabiensis Patton and Culex quinquefasciatus Say in Mwea, Kenya. The sampling unit comprised all non-paddy aquatic habitats and ten randomly selected paddies and canals located within a 200 m radius from the periphery of the study site. Thirteen physico-chemical variables were recorded for each sampling site in each sampling occasion and a sample of mosquito larvae and other aquatic invertebrates collected. The non-paddy aquatic habitats identified included pools and marshes. Morphological identification of 1,974 mosquito larvae yielded four species dominated by Cx. quinquefasciatus (73.2%) and An. arabiensis (25.0%). Pools were associated with significantly higher Cx. quinquefasciatus larval abundance and less diversity of other aquatic invertebrates compared with other habitat types. In contrast, the abundance of An. arabiensis did not differ significantly among habitat types. Culex quinquefasciatus habitats had higher water conductivity and exhibited a higher abundance of other aquatic invertebrates than An. arabiensis habitats. Chi-square analysis indicated that the two species were more likely to coexist in the same habitats than would be expected by chance alone. Anopheles arabiensis larvae were positively associated with dissolved oxygen and adults of family Haliplidae and negatively associated with emergent vegetation and Heptageniidae larvae. Culex quinquefasciatus larvae were positively associated with dissolved oxygen, total dissolved solids, Chironomidae larvae, and Microvelidae adults and negatively associated with emergent vegetation. These findings suggest that both biotic and abiotic factors play a significant role in niche partitioning among Cx. quinquefasciatus and An. arabiensis, a factor that should be considered when designing an integrated vector control program.

Community-directed vector control to supplement mass drug distribution for onchocerciasis elimination in the Madi mid-North focus of Northern Uganda.

BACKGROUND: Onchocerciasis a neglected tropical disease that historically has been a major cause of morbidity and an obstacle to economic development in the developing world. It is caused by infection with Onchocerca volvulus, which is transmitted by black flies of the genus Simulium. The discovery of the potent effect of Mectizan (ivermectin) on O. volvulus microfilariae and the decision by its manufacturer to donate the drug for onchocerciasis spurred the implementation of international programs to control and, more recently, eliminate this scourge. These programs rely primarily on mass distribution of ivermectin (MDA) to the afflicted populations. However, MDA alone will not be sufficient to eliminate onchocerciasis where transmission is intense and where ivermectin MDA is precluded by co-endemicity with Loa loa. Vector control will likely be required as a supplemental intervention in these situations. METHODOLOGY/PRINCIPAL FINDINGS: Because biting by the black fly vectors is often a major nuisance in onchocerciasis afflicted communities, we hypothesized that community members might be mobilized to clear the breeding sites of the vegetation that represents the primary black fly larvae attachment point. We evaluated the effect of such a community based "slash and clear" intervention in multiple communities in Northern Uganda. Slash and Clear resulted in 89-99% declines in vector biting rates. The effect lasted up to 120 days post intervention. CONCLUSIONS/SIGNIFICANCE: Slash and clear might represent an effective, inexpensive, community- based tool to supplement ivermectin distribution as a contributory method to eliminate onchocerciasis and prevent recrudescence.

Larval habitat dynamics and diversity of Culex mosquitoes in rice agro-ecosystem in Mwea, Kenya.

Introduction of irrigation projects in developing nations has often been blamed for aggravating the problem of mosquito-borne diseases by creating ideal larval habitats for vector mosquitoes. However, whereas several studies have demonstrated the relationship between malaria vectors and irrigation, little work has been done on culicine mosquitoes despite their potential in transmission of filariasis and arboviruses and their significant biting nuisance in these areas. This study examined the diversity of Culex mosquito fauna and their larval habitats at two sites (Murinduko and Kiamachiri) in Mwea, Kenya over a 12-month period. The habitat types present at each site within a 200-meter radius around the study village, including randomly selected paddies and canals, were sampled every two weeks to examine the relationship between vegetation cover, water depth, turbidity, and Culex larval counts. Ten culicine species belonging to four genera were identified, with 73.1% of the total collection comprising of Culex duttoni and Cx. quinquefasciatus. Other species collected included Cx. annulioris, Cx. poicilipes, Cx. cinereus, Cx. tigripes, Cx. trifilatus, Aedes spp., Coquilettidia fuscopennata, and Ficalbia splendens. Murinduko was more diverse than Kiamachiri in terms of species richness (10 versus 7 species) and larval habitat diversity (11 versus 8 habitat types). Paddies, canals, and rain pools were the most diverse habitats in terms of species richness, and ditches, rock pools, and tree holes were the least diverse. Principal component and correlation analyses showed a strong association between three Culex species and the measured habitat characteristics. Culex poicilipes was strongly associated with floating vegetation, Cx. annulioris with clean water containing emergent vegetation, and Cx. quinquefasciatus was associated with turbid water. Seasonal changes in larval counts in water reservoirs and pool and ditch habitats were closely associated with rainfall. These findings provide important information on larval habitat preference for different Culex species, which will be useful in designing and implementation of larval control operations.

Environmental abundance of Anopheles (Diptera: Culicidae) larval habitats on land cover change sites in Karima Village, Mwea Rice Scheme, Kenya.

A study was carried out at Karima Village in the Mwea Rice Irrigation Scheme in Kenya to assess the impact of rice husbandry and associated land cover change for mosquito larval abundance. A multi-temporal, land use land cover (LULC) classification dataset incorporating distributions of Anopheles arabiensis aquatic larval habitats was produced in ERDAS Imagine version 8.7 using combined images from IKONOS at 4m spatial resolution from 2005 and Landsat Thematic Mapper (TM)trade mark classification data at 30-meters spatial resolution from 1988 for Karima. Of 207 larval habitats sampled, most were either canals (53.4%) or paddies (45.9%), and only one habitat was classified as a seep (0.5%). The proportion of habitats that were poorly drained was 55.1% compared with 44.9% for the habitats that were well drained. An LULC base map was generated. A grid incorporating each rice paddy was overlaid over the LULC maps stratifying each cell based on levels of irrigation. Paddies/grid cells were classified as 1) well irrigated and 2) poorly irrigated. Early stages of rice growth showed peak larval production during the early part of the cropping cycle (rainy season). Total LULC change for Karima over 16 years was 59.8%. Of those areas in which change was detected, the LULC change for Karima was 4.30% for rice field to built environment, 8.74% for fallow to built environment, 7.19% for rice field to fallow, 19.03% built to fallow, 5.52% for fallow to rice field, and 8.35% for built environment to rice field. Of 207 aquatic habitats in Karima, 54.1 (n = 112) were located in LULC change sites and 45.9 (n = 95) were located in LULC non-change sites. Rice crop LULC maps derived from IKONOS and TM data in geographic information systems can be used to investigate the relationship between rice cultivation practices and higher anopheline larval habitat distribution.

Remote and field level quantification of vegetation covariates for malaria mapping in three rice agro-village complexes in Central Kenya.

BACKGROUND: We examined algorithms for malaria mapping using the impact of reflectance calibration uncertainties on the accuracies of three vegetation indices (VI)'s derived from QuickBird data in three rice agro-village complexes Mwea, Kenya. We also generated inferential statistics from field sampled vegetation covariates for identifying riceland Anopheles arabiensis during the crop season. All aquatic habitats in the study sites were stratified based on levels of rice stages; flooded, land preparation, post-transplanting, tillering, flowering/maturation and post-harvest/fallow. A set of uncertainty propagation equations were designed to model the propagation of calibration uncertainties using the red channel (band 3: 0.63 to 0.69 microm) and the near infra-red (NIR) channel (band 4: 0.76 to 0.90 microm) to generate the Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI). The Atmospheric Resistant Vegetation Index (ARVI) was also evaluated incorporating the QuickBird blue band (Band 1: 0.45 to 0.52 microm) to normalize atmospheric effects. In order to determine local clustering of riceland habitats Gi*(d) statistics were generated from the ground-based and remotely-sensed ecological databases. Additionally, all riceland habitats were visually examined using the spectral reflectance of vegetation land cover for identification of highly productive riceland Anopheles oviposition sites. RESULTS: The resultant VI uncertainties did not vary from surface reflectance or atmospheric conditions. Logistic regression analyses of all field sampled covariates revealed emergent vegetation was negatively associated with mosquito larvae at the three study sites. In addition, floating vegetation (-ve) was significantly associated with immature mosquitoes in Rurumi and Kiuria (-ve); while, turbidity was also important in Kiuria. All spatial models exhibit positive autocorrelation; similar numbers of log-counts tend to cluster in geographic space. The spectral reflectance from riceland habitats, examined using the remote and field stratification, revealed post-transplanting and tillering rice stages were most frequently associated with high larval abundance and distribution. CONCLUSION: NDVI, SAVI and ARVI generated from QuickBird data and field sampled vegetation covariates modeled cannot identify highly productive riceland An. arabiensis aquatic habitats. However, combining spectral reflectance of riceland habitats from QuickBird and field sampled data can develop and implement an Integrated Vector Management (IVM) program based on larval productivity.

Laboratory studies on the effect of inorganic fertilizers on survival and development of immature Culex quinquefasciatus (Diptera: Culicidae).

BACKGROUND & OBJECTIVES: Studies were conducted to determine the effect of ammonium sulfate (AM) and muriate of potash (MOP) fertilizers on survival and development of immature stages of Culex quinquefasciatus Say, a major vector of Bancroftian filariasis in Africa. METHODS: Twenty I instar larvae each were added in four doses of each fertilizer dissolved in one litre of deionised water and in one litre of deionised water as a control in replicates of five. The larvae were monitored every morning throughout their life and the numbers of each instar surviving were recorded. The experiments were discontinued when all the larvae had died or emerged into adults. RESULTS: An analysis of variance test and Tukey's HSD test revealed a significant impact of fertilizers on survival and development of aquatic stages of Cx. quinquefasciatus. Ammonium sulfate accounted for up to 40% mortality rate, and one week delay in development time and this effect was both instar and dose dependent. None of the MOP dosages had significant impact on survival of immatures of Cx. quinquefasciatus and only the higher dosages showed significant impact on development time but in significantly lower magnitudes compared with similar dosages of ammonium sulfate. INTERPRETATION & CONCLUSION: These findings demonstrate the toxic effect of fertilizers on immature stages of Cx. quinquefasciatus contrary to field observations.

A grid-based infrastructure for ecological forecasting of rice land Anopheles arabiensis aquatic larval habitats.

BACKGROUND: For remote identification of mosquito habitats the first step is often to construct a discrete tessellation of the region. In applications where complex geometries do not need to be represented such as urban habitats, regular orthogonal grids are constructed in GIS and overlaid on satellite images. However, rice land vector mosquito aquatic habitats are rarely uniform in space or character. An orthogonal grid overlaid on satellite data of rice-land areas may fail to capture physical or man-made structures, i.e paddies, canals, berms at these habitats. Unlike an orthogonal grid, digitizing each habitat converts a polygon into a grid cell, which may conform to rice-land habitat boundaries. This research illustrates the application of a random sampling methodology, comparing an orthogonal and a digitized grid for assessment of rice land habitats. METHODS: A land cover map was generated in Erdas Imagine V8.7 using QuickBird data acquired July 2005, for three villages within the Mwea Rice Scheme, Kenya. An orthogonal grid was overlaid on the images. In the digitized dataset, each habitat was traced in Arc Info 9.1. All habitats in each study site were stratified based on levels of rice stage RESULTS: The orthogonal grid did not identify any habitat while the digitized grid identified every habitat by strata and study site. An analysis of variance test indicated the relative abundance of An. arabiensis at the three study sites to be significantly higher during the post-transplanting stage of the rice cycle. CONCLUSION: Regions of higher Anopheles abundance, based on digitized grid cell information probably reflect underlying differences in abundance of mosquito habitats in a rice land environment, which is where limited control resources could be concentrated to reduce vector abundance.

Spatially targeting Culex quinquefasciatus aquatic habitats on modified land cover for implementing an Integrated Vector Management (IVM) program in three villages within the Mwea Rice Scheme, Kenya.

BACKGROUND: Continuous land cover modification is an important part of spatial epidemiology because it can help identify environmental factors and Culex mosquitoes associated with arbovirus transmission and thus guide control intervention. The aim of this study was to determine whether remotely sensed data could be used to identify rice-related Culex quinquefasciatus breeding habitats in three rice-villages within the Mwea Rice Scheme, Kenya. We examined whether a land use land cover (LULC) classification based on two scenes, IKONOS at 4 m and Landsat Thematic Mapper at 30 m could be used to map different land uses and rice planted at different times (cohorts), and to infer which LULC change were correlated to high density Cx. quinquefasciatus aquatic habitats. We performed a maximum likelihood unsupervised classification in Erdas Imagine V8.7 and generated three land cover classifications, rice field, fallow and built environment. Differentially corrected global positioning systems (DGPS) ground coordinates of Cx. quinquefasciatus aquatic habitats were overlaid onto the LULC maps generated in ArcInfo 9.1. Grid cells were stratified by levels of irrigation (well-irrigated and poorly-irrigated) and varied according to size of the paddy. RESULTS: Total LULC change between 1988-2005 was 42.1 % in Kangichiri, 52.8 % in Kiuria and and 50.6 % Rurumi. The most frequent LULC changes was rice field to fallow and fallow to rice field. The proportion of aquatic habitats positive for Culex larvae in LULC change sites was 77.5% in Kangichiri, 72.9% in Kiuria and 73.7% in Rurumi. Poorly - irrigated grid cells displayed 63.3% of aquatic habitats among all LULC change sites. CONCLUSION: We demonstrate that optical remote sensing can identify rice cultivation LULC sites associated with high Culex oviposition. We argue that the regions of higher Culex abundance based on oviposition surveillance sites reflect underlying differences in abundance of larval habitats which is where limited control resources could be concentrated to reduce vector larval abundance.

Evaluation of environmental data for identification of Anopheles (Diptera: Culicidae) aquatic larval habitats in Kisumu and Malindi, Kenya.

This research evaluates the extent to which use of environmental data acquired from field and satellite surveys enhances predictions of urban mosquito counts. Mosquito larval habitats were sampled, and multispectral thermal imager (MTI) satellite data in the visible spectrum at 5-m resolution were acquired for Kisumu and Malindi, Kenya, during February and March 2001. All entomological parameters were collected from January to May 2001, June to August 2002, and June to August 2003. In a Poisson model specification, for Anopheles funestus Giles, shade was the best predictor, whereas substrate was the best predictor for Anopheles gambiae, and vegetation for Anopheles arabensis Patton. The top predictors found with a logistic regression model specification were habitat size for An. gambiae Giles, pollution for An. arabensis, and shade for An. funestus. All other coefficients for canopy, debris, habitat nature, permanency, emergent plants, algae, pollution, turbidity, organic materials, all MTI waveband frequencies, distance to the nearest house, distance to the nearest domestic animal, and all land use land cover changes were nonsignificant. MTI data at 5-m spatial resolution do not have an additional predictive value for mosquito counts when adjusted for field-based ecological data.

Validation of a remote sensing model to identify Simulium damnosum s.l. breeding sites in Sub-Saharan Africa.

BACKGROUND: Recently, most onchocerciasis control programs have begun to focus on elimination. Developing an effective elimination strategy relies upon accurately mapping the extent of endemic foci. In areas of Africa that suffer from a lack of infrastructure and/or political instability, developing such accurate maps has been difficult. Onchocerciasis foci are localized near breeding sites for the black fly vectors of the infection. The goal of this study was to conduct ground validation studies to evaluate the sensitivity and specificity of a remote sensing model developed to predict S. damnosum s.l. breeding sites. METHODOLOGY/PRINCIPAL FINDINGS: Remote sensing images from Togo were analyzed to identify areas containing signature characteristics of S. damnosum s.l. breeding habitat. All 30 sites with the spectral signature were found to contain S. damnosum larvae, while 0/52 other sites judged as likely to contain larvae were found to contain larvae. The model was then used to predict breeding sites in Northern Uganda. This area is hyper-endemic for onchocerciasis, but political instability had precluded mass distribution of ivermectin until 2009. Ground validation revealed that 23/25 sites with the signature contained S. damnosum larvae, while 8/10 sites examined lacking the signature were larvae free. Sites predicted to have larvae contained significantly more larvae than those that lacked the signature. CONCLUSIONS/SIGNIFICANCE: This study suggests that a signature extracted from remote sensing images may be used to predict the location of S. damnosum s.l. breeding sites with a high degree of accuracy. This method should be of assistance in predicting communities at risk for onchocerciasis in areas of Africa where ground-based epidemiological surveys are difficult to implement.

Geomapping generalized eigenvalue frequency distributions for predicting prolific Aedes albopictus and Culex quinquefasciatus habitats based on spatiotemporal field-sampled count data.

Marked spatiotemporal variabilities in mosquito infection of arboviruses require adaptive strategies for determining optimal field-sampling timeframes, pool screening, and data analyses. In particular, the error distribution and aggregation patterns of adult arboviral mosquitoes can vary significantly by species, which can statistically bias analyses of spatiotemporal-sampled predictor variables generating misinterpretation of prolific habitat surveillance locations. Currently, there is a lack of reliable and consistent measures of risk exposure based on field-sampled georeferenced explanatory covariates which can compromise quantitative predictions generated from arboviral mosquito surveillance models for implementing larval control strategies targeting productive habitats. In this research we used spatial statistics and QuickBird visible and near-infra-red data for determining trapping sites that were related to Culex quinquefasciatus and Aedes albopictus species abundance and distribution in Birmingham, Alabama. Initially, a Land Use Land Cover (LULC) model was constructed from multiple spatiotemporal-sampled georeferenced predictors and the QuickBird data. A Poisson regression model with a non-homogenous, gamma-distributed mean then decomposed the data into positive and negative spatial filter eigenvectors. An autoregressive process in the error term then was used to derive the sample distribution of the Moran's I statistic for determining latent autocorrelation components in the model. Spatial filter algorithms established means, variances, distributional functions, and pairwise correlations for the predictor variables. In doing so, the eigenfunction spatial filter quantified the residual autocorrelation error in the mean response term of the model as a linear combination of various distinct Cx. quinquefasciatus and Ae. albopictus habitat map patterns. The analyses revealed 18-27% redundant information in the data. Prolific habitats of Cx. quinquefasciatus and Ae. albopictus can be accurately spatially targeted based on georeferenced field-sampled count data using QuickBird data, LULC explanatory covariates, robust negative binomial regression estimates and space-time eigenfunctions.

Describing Anopheles arabiensis aquatic habitats in two riceland agro-ecosystems in Mwea, Kenya using a negative binomial regression model with a non-homogenous mean.

This research illustrates a geostatistical approach for modeling the spatial distribution patterns of Anopheles arabiensis Patton (Patton) aquatic habitats in two riceland environments. QuickBird 0.61 m data, encompassing the visible bands and the near-infra-red (NIR) band, were selected to synthesize images of An. arabiensis aquatic habitats. These bands and field sampled data were used to determine ecological parameters associated with riceland larval habitat development. SAS was used to calculate univariate statistics, correlations and Poisson regression models. Global autocorrelation statistics were generated in ArcGISfrom georeferenced Anopheles aquatic habitats in the study sites. The geographic distribution of Anopheles gambiae s.l. aquatic habitats in the study sites exhibited weak positive autocorrelation; similar numbers of log-larval count habitats tend to clustered in space. Individual rice land habitat data were further evaluated in terms of their covariations with spatial autocorrelation, by regressing them on candidate spatial filter eigenvectors. Each eigenvector generated from a geographically weighted matrix, for both study sites, revealed a distinctive spatial pattern. The spatial autocorrelation components suggest the presence of roughly 14-30% redundant information in the aquatic habitat larval count samples. Synthetic map pattern variables furnish a method of capturing spatial dependency effects in the mean response term in regression analyses of rice land An. arabiensis aquatic habitat data.