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 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 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. 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. 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.
Linearized Models on measles vaccination related centroids in literature cannot provide pertinent data for local government measles managers. Spatial analysis is a cost cutting epidemiological tool for large scale immunization programs. A multivariate regression model was constructed to determine anthropogenic related covariates. In addition, we quantitated the clustering tendencies in the auto- correlated dataset using orthogonal eigenvectors and also illustrated problem hot spots for effective vaccine coverage. Data was retrieved from Demographic Health survey 2013 for Nigeria (N=28,337). Poverty, illiteracy level, and no vitamin A supplements were strong determinants of measles non-vaccination at a statistically significant level of (P<0.0001). The first order autocorrelation statistics (DW=0.1647, P<0.0001), (DW=0.2406, P<0.0001); and second order correlation (Moran’s I=0.456, Z score=1208), (Moran’s I=0.442, Z score=608) demonstrated a positive spatial autocorrelation for rural and urban geo-locations respectively. Land cover land use (LCLU) maps from Google earth and Diva-GIS were uploaded into ArcMap to visually represent the hot spot areas. Significant Mapped data showed that children not vaccinated against measles are clustered in the rural areas of Muslim dominated northern parts of Nigeria.
Untargeted molecular analyses of complex mixtures are relevant for many fields of research, including geochemistry, pharmacology, and medicine. Ultrahigh-resolution mass spectrometry is one of the most powerful tools in this context. The availability of open scripts and online tools for specific data processing steps such as noise removal or molecular formula assignment is growing, but an integrative tool where all crucial steps are reproducibly evaluated and documented is lacking. We developed a novel, server-based tool (ICBM-OCEAN, Institute for Chemistry and Biology of the Marine Environment, Oldenburg-complex molecular mixtures, evaluation & analysis) that integrates published and novel approaches for standardized processing of ultrahigh-resolution mass spectrometry data of complex molecular mixtures. Different from published approaches, we offer diagnostic and validation tools for all relevant steps. Among other features, we included objective and reproducible reduction of noise and systematic errors, spectra recalibration and alignment, and identification of likeliest molecular formulas. With 15 chemical elements, the tool offers high flexibility in formula attribution. Alignment of mass spectra among different samples prior to molecular formula assignment improves mass error and facilitates molecular formula confirmation with the help of isotopologues. The online tool and the detailed instruction manual are freely accessible at www.icbm.de/icbm-ocean.
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.
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.
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.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.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.
Example data for FerryBox Validation use case within immerse-project/Downstream-Users-Toolbox: Analysis and assessment tools from IMMERSE WP8 (github.com) containing exemplary temperature and salinity data from a ship attached FerryBox (https://www.ferrybox.org/) and according surface fields of the the CMEMS Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast model NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013, the high resolution NNEMO.v4.2_RC Southern North Sea configuration developed at Helmholtz-Zentrum Hereon in the context of IMMERSE, and the German Bight configuration operated at Hereon, using the SCHISM unstructured grid moddeling framework for an assesment of the model performance on those variables within the south eastern German Bight on 2018-11-05.
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)™ 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.
Temporally weighted regression models with a spatial autoregressive component may estimate nonlinearities in spatiotemporal-sampled data of Culex quinquefasciatus, a major vector of West Nile Virus (WNV) which can help implement control strategies by determining optimal predictors associated to prolific habitats. The design of this kind of mixed model can specifically incorporate spatial autocorrelation whilst including the influence of other aspatial predictor variables. Currently, the lack of an estimation theory that allows for het- eroscedasticity and corresponding joint hypothesis testing in the presence of spatial dependence in georefer- enced Cx. quinquefasciatus habitat data is a serious shortcoming in WNV research. In this paper we used spatially lagged and simultaneous autoregressive models based on multiple predictor variables of immature Cx. quinquefasciatus and Worldview 1 (WV-1) data to help implant a remote habitat-based surveillance sys- tem in Trinidad. Initially, we used Geomatica Ortho Engine® v. 10.2 for extracting a Digital Elevation Model (DEM) from the WV-1 raw imagery. Results of the DEM analyses indicated a statistically significant inverse linear relationship between total sampled Cx. quinquefasciatus data and elevation (m) (R2 = -0.439; p < 0.0001), with a standard deviation of 10.41. Additional field-sampled information was derived using data from an or-thogonal grid-matrix constructed in an ArcInfo 9.3® and overlaid onto the WV-1 data. A unique identifier was placed in the centroid of each grid cell. Univariate statistics and Poisson regression models were then generated using the georeferenced covariates in SAS/GIS®. Coefficient estimates were also used to define expectations for prior distributions in a Bayesian estimation matrix using Markov Chain Monte Carlo (MCMC) specifications. A spatial residual trend analyses was then performed using autocorrelation indices which linked tabular data in SAS PROCLMIXED® with the egg-raft count data in ArcInfo®. The estimation matrix identified prolific habitats based on the covariate distance to the nearest house. An Ordinary kriged-based interpolator was then constructed in Geostatistical Analyst Extension of ArcGIS 9.3® based on the adjusted Bayesian estimates. For total Cx. quinquefasciatus egg-raft count, first order trend was fitted to the semivariogram at a partial sill of 5.931 km, nugget of 6.374 km, lag size of 7.184 km, and a range of 31.02 km using 12 lags. We assessed the performance accuracy of the interpolation procedures based on the magnitude and distribution of errors between observed and model-predicted values using Voroni tessella- tions. These residuals divided the space between the individual georeferenced Cx. quinquefasciatus habitats by XY coordinates in 2-dimenisional space which revealed that the geophysical parameter error residuals in the interpolation model were within normal statistical limitations. Newer GIS software and WV-1 data can generate highly accurate predictive Cx. quinquefasciatus habitat distribution models which can target prolific habitats of based on field-sampled count data. Our results suggest it may be unnecessary to manage all Cx. quinquefasciatus habitats to obtain significant reductions in incidence and prevalence of WNV in Trinidad.
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