Abstract
In this research, we determined the feasibility of using a Personal Digital Assistant (PDA) as a mobile field data collection system by monitoring mapping and regressing digitized sub-meter resolution polygons of multiple, malaria, mosquito, Anopheline arabiensis s.s., aquatic, larval, habitat covariates. The system employed QuickBird raster imagery displayed on a Trimble Recon X 400 MHz Intel PXA255 Xscale CPU®. The mobile mapping platform was employed to identify specific geographical locations of treated and untreated seasonal An. arabiensis s.s. aquatic larval habitats in Karima rice-village complex in the Mwea Rice Scheme, Kenya. As data pertaining to An. arabiensis s.s. larval habitats were entered, all treated and untreated rice paddies within a 2 km buffer of the agro-village, riceland-complex, epidemiological, study site were viewed and managed on the PDA.
Highlights
Very few mosquito control programs have collected valid outcome metrics that enable public health officials and funding agencies to quantitatively determine the degree and magnitude of their impact on time series, field or remote-sampled aquatic, larval, habitat, endemic, transmission-oriented, explanatory, parameter estimators
When time series field and/or remote sampled geospatial explanatory endemic transmission-oriented data comprise immature counts, especially for rare events, the probability seasonal real-time bidirectional vector mosquito-related aquatic larval habitat georeferencable explanatory risk model may be based upon an auto-Poisson specification, which may be written in the form of approximations in a Personal Digital Assistant (PDA)-geographic information systems (GIS)-Global Positioning System (GPS)-remote sensing (RS) cyber-environment
The root-mean-square deviation (RMSD) represented the sample standard deviation of the differences between predicted values and observed values [5]. These individual differences were represented as the spatiotemporal vector mosquito larval habitat field and remote sampled endemic transmission oriented regression-based residuals when the calculations are performed over the data sample that was used for estimation, and are called prediction errors when computed out-of-sample
Summary
Very few mosquito control programs have collected valid outcome metrics that enable public health officials and funding agencies to quantitatively determine the degree and magnitude of their impact on time series, field or remote-sampled aquatic, larval, habitat, endemic, transmission-oriented, explanatory, parameter estimators. Real-time, bidirectional, PDA-GIS-DGPS-RS cyber-environment, seasonal-sampled, georeferenced, vector, mosquito, aquatic, larval, habitat, explanatory, covariate coefficients rigorously regressed based on a response variable representing total, quantized, larval/pupal, density counts can provide additional autoregressive insights for geospatially, targeting, endemic, transmission-oriented zones (e.g., hyperendemic foci). The values of the time series, georeferencable, explanatory, vector mosquito-related, geo-spatiotemporal, quantitated autocovariate coefficient would depend on the sampled field or remote-specified, parameter estimator values of the response variable in the neighborhood as defined by the PDA-GIS-DGPS-RS cyber-tools This approach has not been employed for biogeographical, risk-based, explanatory, seasonal, real-time, vector, mosquito-related, epidemiological data analyses. Monte Carlo maximum likelihood in a PDA-GIS-DGPS-RS cyber-environment may mathematically contribute to robust, risk-related, cartographic delineations of explanatory, georeferenced, seasonal-sampled, predictive, vector, mosquito-related, real-time, bidirectional operationalizable, eco-epidemiological, endemic, transmission-oriented, risk model residually forecasted derivatives rendered from a time series regression equation. The OLS estimates would involve inverting the geo-spatiotemporal vector malarial mosquito-related
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