Clinical, serological and DNA testing in Bengo Province, Angola further reveals low filarial endemicity and opportunities for disease elimination.

The prevalence of Loa loa, Onchocerca volvulus and Wuchereria bancrofti infections in an under-surveyed area of Bengo Province, Angola, was determined by surveying 22 communities with a combination of clinical, serological and DNA diagnostics. Additional information was collected on participants' duration of residency, access to mass drug administration, knowledge of insect vectors and use of bednets. A total of 1616 individuals (38.1% male: 61.9% female), with an average age of 43 years, were examined. For L. loa, 6.2% (n = 100/16616) individuals were found to have eyeworm, based on the rapid assessment procedure for loiasis (RAPLOA) surveys, and 11.5% (n =178/1543) based on nested PCR analyses of venous blood. L. loa prevalences in long-term residents (>10 years) and older individuals (>60 years) were significantly higher, and older men with eyeworm were better informed about Chrysops vectors. For O. volvulus, 4.7% (n = 74/1567) individuals were found to be positive by enzyme-linked immunosorbent assay (Ov 16 ELISA), with only three individuals reporting to have ever taken ivermectin. For W. bancrofti, no infections were found using the antigen-based immunochromatographic test (ICT) and real-time PCR analysis; however, 27 individuals presented with lymphatic filariasis (LF) related clinical conditions (lymphoedema = 11, hydrocoele = 14, both = 2). Just under half (45.5%) of the participants owned a bednet, with the majority (71.1%) sleeping under it the night before. Our approach of using combination diagnostics reveals the age-prevalence of loiasis alongside low endemicity of onchocerciasis and LF. Future research foci should be on identifying opportunities for more cost-effective ways to eliminate onchocerciasis and to develop innovative surveillance modalities for clinical LF for individual disease management and disability prevention.

Spatial-temporal distribution of Anopheles larval habitats in Uganda using GIS/remote sensing technologies.

BACKGROUND: Anopheles mosquitoes impose an immense burden on the African population in terms of both human health and comfort. Uganda, in particular, boasts one of the highest malaria transmission rates in the world and its entire population is at risk for infection. Despite the immense burden these mosquitoes pose on the country, very few programmes exist that directly combat the issue at the vector control level and even fewer programmes focus on the vector in its most vulnerable juvenile stages. This study utilizes remote sensing techniques and spatial autocorrelation models to identify and prioritize the most prolific Anopheline larval habitats for control purposes in a rural community in Uganda. METHODS: A community-based mosquito surveillance programme was developed and implemented in Papoli Parish in Eastern Uganda over a 4-month period. Each day, a trained field team sampled the larval habitats of Anopheles mosquitoes within the population-dense areas of the community. Habitats and their productivity were identified and plotted spatially on a daily basis. Daily output was combined and displayed as a weekly habitat time-series. Additional spatial analysis was conducted using the Global and Anselin's Local Moran's I statistic to assess habitat spatial autocorrelation. RESULTS: Spatial models were developed to identify highly significant habitats and dictated the priority of these habitats for larval control purposes. Weekly time-series models identified the locations and productivity of each habitat, while Local Moran's I cluster maps identified statistically significant clusters (Cluster: High) and outliers (High Outlier) that were then interpreted for control priority. Models were stitched together in a temporal format to visually demonstrate the spatial shift of statically significant, high priority habitats over the entire study period. DISCUSSION: The findings show that the spatial outcomes of productive habitats can be made starkly apparent through initial habitat modelling and resulting time-series output. However, mosquito control resources are often limited and it is at this point that the Local Moran's I statistics demonstrates its value. Focusing on habitats identified as Cluster: High and High Outlier outputs allow for the identification of the most influential larval habitats. Utilizing this method for malaria control allows for the optimization of control resources in a real time, community driven, fashion, as well as providing a framework for future control practices.

Diversity and Abundance of Mosquitoes Inhabiting Waste Tires in a Subtropical Swamp in Urban Florida.

Automobile tires discarded in urban forest fragments may be a public health hazard, as they can support a population of vector mosquitoes. However, little is known about what factors may affect mosquito abundance and diversity within waste tires in a freshwater wetland forest. This study aimed to determine whether mosquito population dynamics in this environment in Florida differed over a year due to the site of collection and variation in vegetation greenness and elevation. We constructed negative binomial regression models to determine which of these characteristics were significant (α = 0.05) in affecting mosquito count data. Our findings suggest that in this specific environment, none of the covariates scrutinized had significant impacts on modulating overall mosquito and Aedes albopictus (the dominant species) abundance; waste tire habitats in urban freshwater wetland forests may be a year-round public health hazard.

Rapid integrated clinical survey to determine prevalence and co-distribution patterns of lymphatic filariasis and onchocerciasis in a Loa loa co-endemic area: The Angolan experience.

The Republic of Angola is a priority country for onchocerciasis and lymphatic filariasis (LF) elimination, however, the co-distribution of the filarial parasite Loa loa (loiasis) is a significant impediment, due to the risk of severe adverse events (SAEs) associated with ivermectin used in mass drug administration (MDA) campaigns. Angola has a high risk loiasis zone identified in Bengo Province where alternative interventions may need to be implemented; however, the presence and geographical overlap of the three filarial infections/diseases are not well defined. Therefore, this study conducted a rapid integrated filarial mapping survey based on readily identifiable clinical conditions of each disease in this risk zone to help determine prevalence and co-distribution patterns in a timely manner with limited resources. In total, 2007 individuals from 29 communities in five provincial municipalities were surveyed. Community prevalence estimates were determined by the rapid assessment procedure for loiasis (RAPLOA) and rapid epidemiological mapping of onchocerciasis (REMO) together with two questions on LF clinical manifestations (presence of lymphoedema, hydrocoele). Overall low levels of endemicity, with different overlapping distributions were found. Loiasis was found in 18 communities with a prevalence of 2.0% (31/1571), which contrasted to previous results defining the area as a high risk zone. Onchocerciasis prevalence was 5.3% (49/922) in eight communities, and LF prevalence was 0.4% for lymphoedema (8/2007) and 2.6% for hydrocoeles (20/761 males) in seven and 12 communities respectively. The clinical mapping survey method helped to highlight that all three filarial infections are present in this zone of Bengo Province. However, the significant difference in loiasis prevalence found between the past and this current survey suggests that further studies including serological and parasitological confirmation are required. This will help determine levels of infection and risk, understand the associations between clinical, serological and parasitological prevalence patterns, and better determine the most appropriate treatment strategies to reach onchocerciasis and LF elimination targets in the loiasis co-endemic areas. Our results also suggest that the utility of the earlier RAPLOA derived maps, based on surveys undertaken over a decade ago, are likely to be invalid given the extent of population movement and environmental change, particularly deforestation, and that fine scale micro-mapping is required to more precisely delineate the interventions required defined by these complex co-endemicities.

West Nile virus infection rates and avian serology in east-central Illinois.

Understanding the geographic role of different species of mosquito vectors and vertebrate hosts in West Nile virus (WNV) transmission cycles can facilitate the development and implementation of targeted surveillance and control measures. This study examined the relationship between WNV-antibody rates in birds and mosquito infection rates and bloodfeeding patterns in east-central Illinois. The earliest detection of WNV-RNA by reverse transcription-polymerase chain reaction TaqMan was from Culex restuans; however, amplification typically coincided with an increase in abundance of Cx. pipiens. Trap type influenced annual estimates of infection rates in Culex species, as well as estimation of blood meal source. Bird species with the highest WNV-antibody rates (i.e., Mourning Doves [Zenaida macroura], Northern Cardinals [Cardinalis cardinalis], American Robins [Turdus migratorius], and House Sparrows [Passer domesticus]) were also the common species found in Culex blood meals. Although antibody rates were not directly proportional to estimated avian abundance, the apparent availability of mammal species did influence proportion of mammal to bird blood meals. Antibody prevalence in the American Robin was lower than expected based on the strong attraction of Culex to American Robins for blood meals. Age-related differences in serology were evident, antibody rates increased in older groups of robins and sparrows, whereas 1st-year hatch and older adults of Mourning Doves and Northern Cardinals had equally high rates of antibody-positive serum samples. The vector and host interactions observed in east-central Illinois (Champaign County), an urban area surrounded by agriculture, are compared to studies in the densely population areas of southern Cook County.

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.

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.

Generation of monoclonal antibodies to vertebrate albumins for analysis of arthropod blood meals.

An immunoassay using monoclonal antibodies (MAbs) that are specific for different vertebrate taxa (from class to species) has been developed that simplifies and facilitates analysis of vertebrate blood meals from arthropod vectors. The MAbs have been prepared against the single protein albumin, the most abundant protein in vertebrate sera. A panel of these antibodies has been generated against albumins from 33 species of vertebrates, representing four classes, 15 orders, and 25 families. Immunoreactivity of albumin in mosquito blood meals can be detected as late as 48 h after feeding. Immunoassays with MAbs can be carried out in the field as well as the laboratory. Used in conjunction with nucleic acid assays or used alone with an appropriate assortment of antibodies, the assay is simple, sensitive, and unambiguous.

Natural plant sugar sources of Anopheles mosquitoes strongly impact malaria transmission potential.

An improved knowledge of mosquito life history could strengthen malaria vector control efforts that primarily focus on killing mosquitoes indoors using insecticide treated nets and indoor residual spraying. Natural sugar sources, usually floral nectars of plants, are a primary energy resource for adult mosquitoes but their role in regulating the dynamics of mosquito populations is unclear. To determine how the sugar availability impacts Anopheles sergentii populations, mark-release-recapture studies were conducted in two oases in Israel with either absence or presence of the local primary sugar source, flowering Acacia raddiana trees. Compared with population estimates from the sugar-rich oasis, An. sergentii in the sugar-poor oasis showed smaller population size (37,494 vs. 85,595), lower survival rates (0.72 vs. 0.93), and prolonged gonotrophic cycles (3.33 vs. 2.36 days). The estimated number of females older than the extrinsic incubation period of malaria (10 days) in the sugar rich site was 4 times greater than in the sugar poor site. Sugar feeding detected in mosquito guts in the sugar-rich site was significantly higher (73%) than in the sugar-poor site (48%). In contrast, plant tissue feeding (poor quality sugar source) in the sugar-rich habitat was much less (0.3%) than in the sugar-poor site (30%). More important, the estimated vectorial capacity, a standard measure of malaria transmission potential, was more than 250-fold higher in the sugar-rich oasis than that in the sugar-poor site. Our results convincingly show that the availability of sugar sources in the local environment is a major determinant regulating the dynamics of mosquito populations and their vector potential, suggesting that control interventions targeting sugar-feeding mosquitoes pose a promising tactic for combating transmission of malaria parasites and other pathogens.

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.

Anopheles larval abundance and diversity in three rice agro-village complexes Mwea irrigation scheme, central Kenya.

BACKGROUND: The diversity and abundance of Anopheles larvae has significant influence on the resulting adult mosquito population and hence the dynamics of malaria transmission. Studies were conducted to examine larval habitat dynamics and ecological factors affecting survivorship of aquatic stages of malaria vectors in three agro-ecological settings in Mwea, Kenya. METHODS: Three villages were selected based on rice husbandry and water management practices. Aquatic habitats in the 3 villages representing planned rice cultivation (Mbui Njeru), unplanned rice cultivation (Kiamachiri) and non-irrigated (Murinduko) agro-ecosystems were sampled every 2 weeks to generate stage-specific estimates of mosquito larval densities, relative abundance and diversity. Records of distance to the nearest homestead, vegetation coverage, surface debris, turbidity, habitat stability, habitat type, rice growth stage, number of rice tillers and percent Azolla cover were taken for each habitat. RESULTS: Captures of early, late instars and pupae accounted for 78.2%, 10.9% and 10.8% of the total Anopheles immatures sampled (n = 29,252), respectively. There were significant differences in larval abundance between 3 agro-ecosystems. The village with 'planned' rice cultivation had relatively lower Anopheles larval densities compared to the villages where 'unplanned' or non-irrigated. Similarly, species composition and richness was higher in the two villages with either 'unplanned' or limited rice cultivation, an indication of the importance of land use patterns on diversity of larval habitat types. Rice fields and associated canals were the most productive habitat types while water pools and puddles were important for short periods during the rainy season. Multiple logistic regression analysis showed that presence of other invertebrates, percentage Azolla cover, distance to nearest homestead, depth and water turbidity were the best predictors for Anopheles mosquito larval abundance. CONCLUSION: These results suggest that agricultural practices have significant influence on mosquito species diversity and abundance and that certain habitat characteristics favor production of malaria vectors. These factors should be considered when implementing larval control strategies which should be targeted based on habitat productivity and water management.

Accounting for autocorrelation in multi-drug resistant tuberculosis predictors using a set of parsimonious orthogonal eigenvectors aggregated in geographic space.

Spatial autocorrelation is problematic for classical hierarchical cluster detection tests commonly used in multi-drug resistant tuberculosis (MDR-TB) analyses as considerable random error can occur. Therefore, when MDRTB clusters are spatially autocorrelated the assumption that the clusters are independently random is invalid. In this research, a product moment correlation coefficient (i.e., the Moran's coefficient) was used to quantify local spatial variation in multiple clinical and environmental predictor variables sampled in San Juan de Lurigancho, Lima, Peru. Initially, QuickBird 0.61 m data, encompassing visible bands and the near infra-red bands, were selected to synthesize images of land cover attributes of the study site. Data of residential addresses of individual patients with smear-positive MDR-TB were geocoded, prevalence rates calculated and then digitally overlaid onto the satellite data within a 2 km buffer of 31 georeferenced health centers, using a 10 m2 grid-based algorithm. Geographical information system (GIS)-gridded measurements of each health center were generated based on preliminary base maps of the georeferenced data aggregated to block groups and census tracts within each buffered area. A three-dimensional model of the study site was constructed based on a digital elevation model (DEM) to determine terrain covariates associated with the sampled MDR-TB covariates. Pearson's correlation was used to evaluate the linear relationship between the DEM and the sampled MDR-TB data. A SAS/GIS(R) module was then used to calculate univariate statistics and to perform linear and non-linear regression analyses using the sampled predictor variables. The estimates generated from a global autocorrelation analyses were then spatially decomposed into empirical orthogonal bases using a negative binomial regression with a non-homogeneous mean. Results of the DEM analyses indicated a statistically non-significant, linear relationship between georeferenced health centers and the sampled covariate elevation. The data exhibited positive spatial autocorrelation and the decomposition of Moran's coefficient into uncorrelated, orthogonal map pattern components revealed global spatial heterogeneities necessary to capture latent autocorrelation in the MDR-TB model. It was thus shown that Poisson regression analyses and spatial eigenvector mapping can elucidate the mechanics of MDR-TB transmission by prioritizing clinical and environmental-sampled predictor variables for identifying high risk populations.

Population genetic structure of Anopheles arabiensis (Diptera: Culicidae) in a rice growing area of central Kenya.

Studies were conducted to examine the population genetic structure of Anopheles arabiensis (Patton) in Mwea Rice Irrigation Scheme and surrounding areas in Central Kenya, under different agricultural systems. This study was motivated by observed differences in malaria transmission indices of An. arabiensis within the scheme compared with adjacent nonirrigated areas. Agricultural practices can modify local microclimate and influence the number and diversity of larval habitats and in so doing may occasion subpopulation differentiation. Thirty samples from each of the three study sites were genotyped at eight microsatellite loci. Seven microsatellite loci showed high polymorphism but revealed no genetic differentiation (FST = 0.006, P = 0.312) and high gene flow (Nm = 29-101) among the three populations. Genetic bottleneck analysis showed no indication of excess heterozygosity in any of the populations. There was high frequency of rare alleles, suggesting that An. arabiensis in the study area has a high potential of responding to selective pressures from environmental changes and vector control efforts. These findings imply that An. arabiensis in the study area occurs as a single, continuous panmictic population with great ability to adapt to human-imposed selective pressures.

Interspecies predation between Anopheles gambiae s.s. and Culex quinquefasciatus larvae.

Interaction of aquatic stages of coexisting mosquito species may have significant influence on resulting adult mosquito populations. We used two coexisting species, Anopheles gambiae s.s. and Culex quinquefasciatus to investigate whether third instars of one species consumed first instars of the other. First instars of one species were readily consumed by a third instar of the other species irrespective food quantity. DNA of Cx. quinquefasciatus was detected in the eight An. gambiae s.s. third instars presumed to have consumed at least one Cx. quinquefasciatus first instar. Likewise, DNA of An. gambiae s.s. was detected in five of eight Cx. quinquefasciatus third instars presumed to have consumed at least one An. gambiae s.s. first instar. A small number of dead first instars was found in the controls indicating that some larvae in the treatment group may have been consumed after they had died. These findings suggest that intraguild predation between the two species may be common in nature and that it is a facultative process that is not induced by food shortage. The findings further suggest that polymerase chain reaction could be a useful technique in the study of this phenomenon in natural habitats.

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.

Predicting the impact of insecticide-treated bed nets on malaria transmission: the devil is in the detail.

BACKGROUND: Insecticide-treated bed nets (ITNs), including long-lasting insecticidal nets (LLINs), play a primary role in global campaigns to roll back malaria in tropical Africa. Effectiveness of treated nets depends on direct impacts on individual mosquitoes including killing and excite-repellency, which vary considerably among vector species due to variations in host-seeking behaviours. While monitoring and evaluation programmes of ITNs have focuses on morbidity and all-cause mortality in humans, local entomological context receives little attention. Without knowing the dynamics of local vector species and their responses to treated nets, it is difficult to predict clinical outcomes when ITN applications are scaled up across African continent. Sound model frameworks incorporating intricate interactions between mosquitoes and treated nets are needed to develop the predictive capacity for scale-up applications of ITNs. METHODS: An established agent-based model was extended to incorporate the direct outcomes, e.g. killing and avoidance, of individual mosquitoes exposing to ITNs in a hypothetical village setting with 50 houses and 90 aquatic habitats. Individual mosquitoes were tracked throughout the life cycle across the landscape. Four levels of coverage, i.e. 40, 60, 80 and 100%, were applied at the household level with treated houses having only one bed net. By using Latin hypercube sampling scheme, parameters governing killing, diverting and personal protection of net users were evaluated for their relative roles in containing mosquito populations, entomological inoculation rates (EIRs) and malaria incidence. RESULTS: There were substantial gaps in coverage between households and individual persons, and 100% household coverage resulted in circa 50% coverage of the population. The results show that applications of ITNs could give rise to varying impacts on population-level metrics depending on values of parameters governing interactions of mosquitoes and treated nets at the individual level. The most significant factor in determining effectiveness was killing capability of treated nets. Strong excito-repellent effect of impregnated nets might lead to higher risk exposure to non-bed net users. CONCLUSION: With variabilities of vector mosquitoes in host-seeking behaviours and the responses to treated nets, it is anticipated that scale-up applications of INTs might produce varying degrees of success dependent on local entomological and epidemiological contexts. This study highlights that increased ITN coverage led to significant reduction in risk exposure and malaria incidence only when treated nets yielded high killing effects. It is necessary to test efficacy of treated nets on local dominant vector mosquitoes, at least in laboratory, for monitoring and evaluation of ITN programmes.

Patterns of Plasmodium vivax and Plasmodium falciparum malaria underscore importance of data collection from private health care facilities in India.

BACKGROUND: This study describes patterns of falciparum and vivax malaria in a private comprehensive-care, multi-specialty hospital in New Delhi from July 2006 to July 2008. METHODS: Malarial morbidity by Plasmodium species (Plasmodium falciparum, Plasmodium vivax, or Plasmodium sp.) was confirmed using microscopy and antigen tests. The influence of seasonal factors and selected patient demographics on morbidity was evaluated. The proportions of malaria cases caused by P. falciparum at the private facility were compared to data from India's National Vector Borne Disease Control Programme (NVBDCP) during the same period for the Delhi region. RESULTS: In New Delhi, P. faciparum was the dominant cause of cases requiring treatment in the private hospital during the period examined. The national data reported a smaller proportion of malaria cases caused by P. falciparum in the national capital region than was observed in a private facility within the region. Plasmodium vivax also caused a large proportion of the cases presenting clinically at the private hospital during the summer and monsoon seasons. CONCLUSION: The proportion of P. falciparum malaria cases tends to be greatest during the post-monsoon season while the proportion of P. vivax malaria cases tends to be greatest in the monsoon season. Private hospital data demonstrate an under-reporting of malaria case incidences in the data from India's national surveillance programme during the same period for the national capital region.

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.

Developing operational algorithms using linear and non-linear squares estimation in Python for the identification of Culex pipiens and Culex restuans in a mosquito abatement district (Cook County, Illinois, USA).

In this research, community level spatial models were developed for determining mosquito abundance and environmental factors that could aid in the risk prediction of West Nile virus (WNv) outbreaks. Adult Culex pipiens and Culex restuan mosquitoes and multiple habitat covariates were collected from nine sites within Cook County, Illinois, USA, to provide spatio-temporal information on the abundance of WNv vectors from 2002 to 2005. Regression analyses of the sampled covariates revealed that the adult Culex population was positively associated with temperature throughout the sampling frame. The model output also indicated that precipitation was negatively associated to mosquito abundance in 2002, 2003 and 2005 (P <0.05), but positively associated in 2004 (P <0.05). A land use land cover classification, based on QuickBird visible and near infra-red data, acquired at 0.61 m resolution, was used to investigate possible associations between geographical features and the abundance of sampled Culex oviposition surveillance sites. A maximum likelihood unsupervised classification in ArcInfo 9.2(R) revealed that the highest overall mosquito abundance was found in sites having a low-to-moderate range of built environment (40%) and high forest composition. A set of propagation equations were then designed to model the calibration uncertainties, which revealed that normalized difference vegetation index (NDVI), and two NDVI variants, were informative markers for the sampled mosquito data. Spatial dependence of the covariates of Cx. restuans and Cx. pipiens oviposition sites were indexed using semivariograms, which suggested that all main effects of the explanatory variables were statistically significant in the model. Additionally, a multispectral classification and digital elevation model-based geographical information system method were able to evaluate stream flow direction and accumulation for identification of terrain covariates associated with the sampled habitat data. These results demonstrate that remotely sensed operational indices can be used to identify parameters associated with field-sampled Cx. pipiens and Cx. restuans aquatic habitats.