Effects of a botanical larvicide derived from Azadirachta indica (the neem tree) on oviposition behaviour in Anopheles gambiae s.s. mosquitoes

More focus is given to mosquito larval control due to the necessity to use several control techniques together in integrated vector management programmes. Botanical products are thought to be able to provide effective, sustainable and cheap mosquito larval control tools. However, bio-larvicides like Azadirachta indica (neem) could repel adult mosquitoes from laying their eggs in the treated larval habitats. In this study the response of Anopheles gambiae s.s. mosquitoes towards varying doses of crude aqueous neem extracts was examined. Non-choice oviposition tests were used to measure the proportion of mosquitoes laying on the first or second night, or not laying at all, when compared to the control. For each individual mosquito, the number of eggs laid and/or retained in the ovary was counted to determine the relationship between wing length and egg production. Larger female mosquitoes produced larger egg batches. The results show that at a dose of 0.1 g/l, a concentration previously found to be effective at controlling mosquito larvae, the oviposition behaviour of adult female mosquitoes was not significantly affected. The results indicate that the mosquitoes would expose progeny to this neem control tool, making the use of these simple neem wood extracts effective and potentially sustainable

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

<p>Abstract</p> <p>Background</p> <p>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 <it>Anopheles arabiensis </it>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 μm) and the near infra-red (NIR) channel (band 4: 0.76 to 0.90 μm) 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 μm) to normalize atmospheric effects. In order to determine local clustering of riceland habitats <it>Gi*(d) </it>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 <it>Anopheles </it>oviposition sites.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>NDVI, SAVI and ARVI generated from QuickBird data and field sampled vegetation covariates modeled cannot identify highly productive riceland <it>An. arabiensis </it>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.</p

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

Spatial distribution and habitat characterisation of Anopheles larvae along the Kenyan coast

Background & objectives: A study was conducted to characterise larval habitats and to determine spatialheterogeneity of the Anopheles mosquito larvae. The study was conducted from May to June 1999 innine villages along the Kenyan coast.Methods: Aquatic habitats were sampled by use of standard dipping technique. The habitats werecharacterised based on size, pH, distance to the nearest house, coverage of canopy, surface debris, algaeand emergent plants, turbidity, substrate, and habitat type.Results: A total of 110 aquatic habitats like stream pools (n = 10); puddles (n = 65); tire tracks (n =5); ponds (n = 5) and swamps (n = 25) were sampled in nine villages located in three districts of theKenyan coast. A total of 7,263 Anopheles mosquito larvae were collected, 63.9% were early instarsand 36.1% were late instars. Morphological identification of the III and IV instar larvae by use ofmicroscopy yielded 90.66% (n = 2,377) Anopheles gambiae Complex, 0.88% (n = 23) An. funestus,An. coustani 7.63% (n = 200), An. rivulorum 0.42% (n = 11), An. pharoensis 0.19% (n = 5), An.swahilicus 0.08% (n = 2), An. wilsoni 0.04% (n = 1) and 0.11% (n = 3) were unidentified. A subset ofthe An. gambiae Complex larvae identified morphologically, was further analysed using rDNA-PCRtechnique resulting in 68.22% (n = 1,290) An. gambiae s.s., 7.93% (n = 150) An. arabiensis and 23.85%(n = 451) An. merus. Multiple logistic regression model showed that emergent plants (p = 0.019), andfloating debris (p = 0.038) were the best predictors of An. gambiae larval abundance in these habitats.Interpretation & conclusion: Habitat type, floating debris and emergent plants were found to be thekey factors determining the presence of Anopheles larvae in the habitats. For effective larval control,the type of habitat should be considered and most productive habitat type be given a priority in themosquito abatement programm

Plasmodium falciparum gametocyte carriage in asymptomatic children in western Kenya

BACKGROUND: Studies on Plasmodium falciparum gametocyte development and dynamics have almost exclusively focused on patients treated with antimalarial drugs, while the majority of parasite carriers in endemic areas are asymptomatic. This study identified factors that influence gametocytaemia in asymptomatic children in the absence and presence of pyrimethamine-sulphadoxine (SP) antimalarial treatment. METHODS: A cohort of 526 children (6 months – 16 years) from western Kenya was screened for asexual parasites and gametocytes and followed weekly up to four weeks. Children with an estimated parasitaemia of ≥1,000 parasites/μl were treated with SP according to national guidelines. Factors associated with gametocyte development and persistence were determined in untreated and SP-treated children with P. falciparum mono-infection. RESULTS: Gametocyte prevalence at enrolment was 33.8% in children below five years of age and decreased with age. In the absence of treatment 18.6% of the children developed gametocytaemia during follow-up; in SP-treated children this proportion was 29.8%. Age, high asexual parasite density and gametocyte presence at enrolment were predictive factors for gametocytaemia. The estimated mean duration of gametocytaemia for children below five, children from five to nine and children ten years and above was 9.4, 7.8 and 4.1 days, respectively. CONCLUSION: This study shows that a large proportion of asymptomatic untreated children develop gametocytaemia. Gametocytaemia was particularly common in children below five years who harbor gametocytes for a longer period of time. The age-dependent duration of gametocytaemia has not been previously shown and could increase the importance of this age group for the infectious reservoir

Behavioural determinants of gene flow in malaria vector populations: Anopheles gambiae males select large females as mates

BACKGROUND: Plasmodium-refractory mosquitoes are being rapidly developed for malaria control but will only succeed if they can successfully compete for mates when released into the wild. Pre-copulatory behavioural traits maintain genetic population structure in wild mosquito populations and mating barriers have foiled previous attempts to control malaria vectors through sterile male release. METHODS: Varying numbers of virgin male and female Anopheles gambiae Giles, from two strains of different innate sizes, were allowed to mate under standardized conditions in laboratory cages, following which, the insemination status, oviposition success and egg batch size of each female was assessed. The influence of male and female numbers, strain combination and female size were determined using logistic regression, correlation analysis and a simple mechanistic model of male competition for females. RESULTS: Male An. gambiae select females on the basis of size because of much greater fecundity among large females. Even under conditions where large numbers of males must compete for a smaller number of females, the largest females are more likely to become inseminated, to successfully oviposit and to produce large egg batches. CONCLUSIONS: Sexual selection, on the basis of size, could either promote or limit the spread of malaria-refractory genes into wild populations and needs to be considered in the continued development and eventual release of transgenic vectors. Fundamental studies of behavioural ecology in malaria vectors such as An. gambiae can have important implications for malaria control and should be prioritised for more extensive investigation in the future

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

<p>Abstract</p> <p>Background</p> <p>The aim of this study was to determine if remotely sensed data and Digital Elevation Model (DEM) can test relationships between <it>Culex quinquefasciatus </it>and <it>Anopheles gambiae </it>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 <it>Cx. quinquefasciatus </it>and <it>An. gambiae </it>s.l. The aquatic habitat dataset were overlaid onto Land Use Land Cover (LULC) maps retrieved from Landsat imagery with 150 m × 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 <it>I </it>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 <it>Cx. quinquefasciatus </it>and <it>An. gambiae </it>s.l. and abundance.</p> <p>Results</p> <p>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 <it>An. gambiae </it>s.l. larvae were associated with shade while <it>Cx. quinquefasciatus </it>were associated with floating vegetation. Moran's <it>I </it>and the General G statistics for mosquito density by species and instars, identified significant clusters of high densities of <it>Anopheles</it>; larvae, however, <it>Culex </it>are not consistently clustered. A stepwise negative binomial regression decomposed the immature <it>An. gambiae </it>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 <it>Culex </it>(0.24) while for <it>Anopheles </it>there was a negative correlation (-0.23) for a local model distance to stream.</p> <p>Conclusion</p> <p>These data demonstrate that optical remote sensing; geostatistics and DEMs can be used to identify parameters associated with <it>Culex </it>and <it>Anopheles </it>aquatic habitats.</p

Survival of immature Anopheles arabiensis (Diptera: Culicidae) in aquatic habitats in Mwea rice irrigation scheme, central Kenya

BACKGROUND: The survivorship and distribution of Anopheles arabiensis larvae and pupae was examined in a rice agro-ecosystem in Mwea Irrigation Scheme, central Kenya, from August 2005 to April 2006, prior to implementation of larval control programme. METHODS: Horizontal life tables were constructed for immatures in semi-field condition. The time spent in the various immature stages was determined and survival established. Vertical life tables were obtained from five paddies sampled by standard dipping technique. RESULTS: Pre-adult developmental time for An. arabiensis in the trays in the experimental set up in the screen house was 11.85 days from eclosion to emergence. The mean duration of each instar stage was estimated to be 1.40 days for first instars, 2.90 days for second instars, 1.85 days for third instars, 3.80 days for fourth instars and 1.90 days for pupae. A total of 590 individuals emerged into adults, giving an overall survivorship from L1 to adult emergence of 69.4%. A total of 4,956 An. arabiensis immatures were collected in 1,400 dips throughout the sampling period. Of these, 55.9% were collected during the tillering stage, 42.5% during the transplanting period and 1.6% during the land preparation stage. There was a significant difference in the An. arabiensis larval densities among the five stages. Also there was significant variation in immature stage composition for each day's collection in each paddy. These results indicate that the survival of the immatures was higher in some paddies than others. The mortality rate during the transplanting was 99.9% and at tillering was 96.6%, while the overall mortality was 98.3%. CONCLUSION: The survival of An. arabiensis immatures was better during the tillering stage of rice growth. Further the survival of immatures in rice fields is influenced by the rice agronomic activities including addition of nitrogenous fertilizers and pesticides. For effective integrated vector management, the application of larvicides should target An. arabiensis larvae at the tillering stage (early vegetative stage of rice) when their survival in the aquatic habitats is high to significantly reduce them and the larvicides should be long-lasting to have a significant impact on the malaria vector productivity on the habitats

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

<p>Abstract</p> <p>Background</p> <p>Autoregressive regression coefficients for <it>Anopheles arabiensis </it>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 <it>An. arabiensis </it>aquatic habitats of acceptable prediction. It is therefore useful to conduct some form of spatial error analysis to detect clusters of <it>An. arabiensis </it>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 <it>Anopheles </it>aquatic habitat covariates. A test for diagnostic checking error residuals in an <it>An. arabiensis </it>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>By specifying coefficient estimates in a Bayesian framework, the covariate number of tillers was found to be a significant predictor, positively associated with <it>An. arabiensis </it>aquatic habitats. The spatial filter models accounted for approximately 19% redundant locational information in the ecological sampled <it>An. arabiensis </it>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.</p> <p>Conclusion</p> <p>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 <it>An. arabiensis </it>aquatic habitats based on larval/pupal productivity.</p