Benjamin George Jacob

Negative spatial autocorrelation (NSA), the tendency for dissimilar neighboring values to cluster on a map, may go undetected in statistical analyses of immature Anopheles gambiae s.l., a leading malaria mosquito vector in Sub-Saharan Africa. Unquantified NSA generated from an inverse variance-covariance matrix may generate misspecifications in an An. gambiae s.l. habitat model. In this research, we used an eigenfunction decomposition algorithm based on a modified geographic connectivity matrix to compute the Moran's I statistic, to uncover hidden NSA in a dataset of georeferenced An. gambiae s.l. habitat explanatory predictor variables spatiotemporally sampled in Malindi and Kisumu, Kenya. The Moran's I statistic was decomposed into orthogonal synthetic map patterns. Global tests revealed that |z_MC|s generated were less than 1.11 for the presence of latent autocorrelation. The algorithm captured NSA in the An. gambiae s.l. habitat data by quantifying all non-normal random variables, space-time heterogeneity, and distributional properties of the spatial filters.

Reliable models of the transmission intensity of malaria, based on vector mosquito aquatic habitat larval productivity, are urgently needed, especially in endemic areas of Sub-Saharan Africa (SSA). Such models are fundamental for estimating the scale of the problem, and, hence, the resources needed to combat malaria in urban environments. These models also provide benchmarks for assessing the progress of control and indicate the geographical regions that should be prioritized. In this research, individual urban aquatic habitats of Anopheles gambiae s.l., a major malaria vector in SSA, were examined in terms of their spatial covariations by modelling ecologically sampled predictor variables within a Bayesian framework. Field sampling was conducted in two urban environments in Kenya, from July 2005 to December 2006. QuickBird satellite data, encompassing visible and near-infrared (NIR) bands, were selected to synthesize images of An. gambiae s.l. aquatic habitats. Statistical Analysis Software (SAS) was used to explore univariate statistics, correlations and distributions, and to perform Poisson regression analyses. These preliminary tests showed good type I error control mechanisms and precise parameter estimates. The model coefficients were then used to define expectations for prior distributions in a Markov chain Monte Carlo (MCMC) analysis. By specifying coefficient estimates in a Bayesian framework, depth of habitat was found to be a significant predictor, positively associated with urban An. gambiae s.l. aquatic habitats. There was no significant autocorrelation present in either the residual error or the predictor variable depth of habitat.

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.

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.

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 μm) 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®.

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.

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)

The aim of this study was to determine whether areas of high Culex pipiens and Culex restuans abundance in an urban environment, based on fixed oviposition surveillance sites, corresponded to remotely sensed data. A land use land cover (LULC) classification, based on Landsat‐7 Enhanced Thematic Mapper Plus (ETM+) data acquired in July 2003 and Landsat‐5 TM data from July 1991, was compared to the abundance of Culex egg rafts in Urbana‐Champaign, East‐Central Illinois. We performed a maximum likelihood, unsupervised classification and generated three land cover classifications: urban, non‐urban and water. Ground coordinates of the Culex surveillance sites were overlaid onto LULC maps with 10 m² grid cells. The grid was stratified based on levels of drainage: 0 = poorly drained and 1 = well‐drained. Total LULC change from 1991 to 2003 in the Urbana‐Champaign study site was relatively low, at 12.1%. The most frequent LULC category was maintained urban. The egg raft rate was significantly higher in urban LULC habitats. Remote stratification of the urban land cover using QuickBird visible and near‐infrared (NIR) data revealed that high‐density canopy coverage was most frequently associated with high Culex abundance in oviposition traps. We demonstrate that optical remote sensing can identify land use parameters associated with high Culex oviposition.