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

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

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 multidrug resistant tuberculosis (MDR-TB) analyses as considerable random error can occur. Therefore, when MDR-TB 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 (spatial resolution = 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 centres, using a 10 m2 grid-based algorithm. Geographical information system (GIS)- gridded measurements of each health centre 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 MDRTB covariates. Pearson’s correlation was used to evaluate the linear relationship between the DEM and the sampled MDR-TB data. A SAS/GIS® 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 centres and the sampled covariate elevation. The data exhibited positive spatial autocorrelation and the decomposition of Moran’s coefficient into uncorrelated, orthogonal map pattern components which 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

Use of climate in a simple entomological framework to improve dynamic simulation and forecast of malaria transmission

Malaria is a serious and life-threatening mosquito-borne disease that every year affects over 200 million individuals and causes 400,00 deaths. An additional 0.5 billion people globally are at risk of malaria infection. The unique role of climate in influencing malaria transmission outcomes across individual communities by acting on multiple dimensions of the malaria vector and parasite ecology has been long recognized. This recognition has led to the development of explicit and implicit climate-driven models of malaria transmission designed to better understand and predict patterns of population vulnerability and uncover potential challenges to malaria control. However, existing implicitly-forced process-based models of malaria have relied on indirectly correlated predictors of malaria transmission, instead of direct relationships among climate, vector entomology and parasite ecology. The lack of biologically-motivated modulation of malaria transmission compromises meaningful interpretation of the ecological role played by climate in malaria transmission. Similarly, the specific influence of climate on vector and parasite dynamics is obscured, limiting the utility of these simple and powerful model forms. This dissertation focuses on elaborating the direct ecological relationships between climate, the malaria vector and parasite to enhance the ecological utility of lower dimensional mathematical models of malaria transmission. In the 2nd chapter of this thesis, a climate-driven entomological modeling framework is developed, consisting of a simple dynamic model that explicitly tracks malaria transmission in human populations and implicitly represents the malaria force of infection through climate-regulation of multiple aspects of the Entomological Inoculation Rate (EIR). The EIR-model construct is found to accurately capture seasonal malaria dynamics under free-simulation, when coupled to local rainfall and temperature climatology across multiple local regions in Rwanda. Furthermore, local rainfall modulation of sub-adult survivorship is found to be a more critical driver of seasonal malaria dynamics than other environmentally-regulated components of EIR. In chapter 3, the model framework is paired with data assimilation methods to dynamically simulate interannual malaria incidence in Rwanda, infer parameters of malaria transmission and validate the malaria model. Results indicate that the implicitly-forced transmission model is able to reproduce interannual and seasonal malaria incidence at regional and local scales. However, accuracy of model description of malaria incidence is more varied at the more resolved local level. Intensified malaria control efforts during the later years of the study are suspected to increase the discrepancy between the vector and parasite dynamics dictated by climate and the observed widespread decline in malaria activity in the region. Nonetheless, the parameters of transmission identified across populations in Rwanda were comparable to existing estimates of malaria, further validating the transmission model and data assimilation approach. For the 4th chapter, a state-of-the-art Bayesian inference forecasting system for the EIR-model framework is developed, as well as a multi-model forecasting system consisting of weighted-average predictions from the dynamic malaria model and historical expectance predictions. Retrospective forecasts of four years of malaria data from 42 regions in Rwanda indicate that the model-inference forecasting system predicts malaria incidence more accurately than historical expectance alone, particularly for predictions with 1-6 weeks lead times. Although slightly less skillful, the multi-model system was found to substantively enhance forecast reliability of the EIR-model system, bolstering the utility of the malaria model as a robust forecaster of malaria in the region. The concluding chapter describes areas for improving the specification of the parsimonious model construct. The need to include malaria control coverage data as exogenous forces of transmission, non-climate drivers and alternate sources of climate exposure that support transmission are highlighted. Future works on forecast calibration needed to improve model performance for real-time prediction are also detailed. In addition, areas for application within information systems for evaluating malaria risk and for advising malaria control efforts, specifically relating to local variability in malaria burden and characterization of entomological drivers of local malaria, are identified and further discussed. The model systems developed in this thesis advance the capabilities of lower dimension dynamic models to connect the ecological drivers of malaria transmission to climate variation. Such process-based formulations could provide better climate-driven descriptions of malaria, while limiting model complexity, without compromising representation of entomological relationships that are potentially valuable for improved understanding and control of malaria transmission

A stochastic model of malaria transmission

Malaria models have evolved since Ross and Macdonald. By using an agent-based stochastic model we have looked into di erent aspects of disease transmission: 1. Gametocytemia phase transition between epidemic stability and disease elimination, and the potential bene t of combining gametocidal agents and ivermectin. 2. Heterogeneity promotes disease spreading. 3. Disease supression from the combined use of ivermectin and primaquine. 4. Utility of Hurst exponent and Shannon entropy in malaria forecasting. Results and conclusion: Malaria transmission was simulated with a computational agent-based model assuming a small African village. We have con rmed gametocytemia as a critical factor in disease transmission, revealing an abrupt phase transition between epidemic stability and disease elimination [326]. We have also found that synergism between gametocidal agents (primaquine) and ivermectin (a selective Anophelocide drug a ecting parasite maturation after mosquito infection) could e ectively suppress human-to-mosquito disease transmission [326]. We have found that heterogeneity ampli es disease transmission (roughly three times in our model). Different aspects of heterogeneity were analyzed such as human migration, mosquito density, and rainfall [327]. We have con rmed the potential bene t of suppressing heterogeneity-induced disease transmission with the use of gametocidal agents and ivermectin. Hurst exponent has been used in hydrology and in the stock market. No previous evidence of its application to infectious theory has been found. Yet, our data suggests that Hurst exponent and information entropy could be useful in malaria forecasting [328]. Our results support the combined use of gametocidal agents (primaquine or methylene blue) and ivermectin as part of an integrated approach to malaria.Os modelos de malária são úteis desde Ross e Macdonald. Através de um modelo estocástico de agente, foram analisados vários aspectos da transmissão da malária: 1. A existência de uma transição de fase entre estabilidade e eliminação da doença em função da gametocitemia. 2. O uso combinado de fármacos gametocidas e ivermectina na redução da transmissão. 3. O papel da heterogeneidadena propagação da malária. 4. A utilidade do expoente de Hurst e da entropia de Shannon na previão da malária. Resultados e conclusões: Foi utilizado um modelo computacional de agente com simulação da transmissão de malária numa pequena aldeia africana. Confirmámos a gametocitemia como um factor crítico na propagação da malária demonstrando uma transição abrupta de fase entre estabilidade epidémica e eliminação da doença. No nosso modelo foi demonstrado que na presença de heterogeneidade a transmissão de malária pode sofrer uma amplificação significativa, de aproximadamente três vezes. Foram analisados diferentes aspectos da heterogeneidade tais como a migração humana, a densidade vectorial e a precipitação sazonal. Foi confirmado o potencial benefício de supressão da transmissão da malária na presença de heterogeneidade com a utilização de fármacos gametocidas (primaquina) e ivermectina. O expoente de Hurst tem sido aplicado com sucesso nas áreas da hidrologia e do mercado bolsista. Não houve até agora evidência da sua aplicação à área da infecciologia. No entanto, os dados apresentados sugerem a sua utilidade, a par da entropia de Shannon, na previsão da incidência da malária. Foi demonstrado que o uso combinado de agentes gametocidas (primaquina ou azul de metileno) e ivermectina pode constituir uma abordagem eficaz na prevenção da malári

Translational Research for Zoonotic Parasites: New Findings toward Improved Diagnostics, Therapy and Prevention

In this book is reported novel information on diagnosis, treatment, and control of parasites that are naturally transmitted from animal reservoirs to humans. Subjects: Public Health and Healthcare: Prevention; Medicine and Pharmacology: Therapy

African Handbook of Climate Change Adaptation

This open access book discusses current thinking and presents the main issues and challenges associated with climate change in Africa. It introduces evidences from studies and projects which show how climate change adaptation is being - and may continue to be successfully implemented in African countries. Thanks to its scope and wide range of themes surrounding climate change, the ambition is that this book will be a lead publication on the topic, which may be regularly updated and hence capture further works. Climate change is a major global challenge. However, some geographical regions are more severly affected than others. One of these regions is the African continent. Due to a combination of unfavourable socio-economic and meteorological conditions, African countries are particularly vulnerable to climate change and its impacts. The recently released IPCC special report "Global Warming of 1.5º C" outlines the fact that keeping global warming by the level of 1.5º C is possible, but also suggested that an increase by 2º C could lead to crises with crops (agriculture fed by rain could drop by 50% in some African countries by 2020) and livestock production, could damage water supplies and pose an additonal threat to coastal areas. The 5th Assessment Report produced by IPCC predicts that wheat may disappear from Africa by 2080, and that maize— a staple—will fall significantly in southern Africa. Also, arid and semi-arid lands are likely to increase by up to 8%, with severe ramifications for livelihoods, poverty eradication and meeting the SDGs. Pursuing appropriate adaptation strategies is thus vital, in order to address the current and future challenges posed by a changing climate. It is against this background that the "African Handbook of Climate Change Adaptation" is being published. It contains papers prepared by scholars, representatives from social movements, practitioners and members of governmental agencies, undertaking research and/or executing climate change projects in Africa, and working with communities across the African continent. Encompassing over 100 contribtions from across Africa, it is the most comprehensive publication on climate change adaptation in Africa ever produced

Triple Helix as a Strategic Tool to Fast-Track Climate Change Adaptation in Rural Kenya: Case Study of Marsabit County

AbstractThe lack of affordable, clean, and reliable energy in Africa's rural areas forces people to resort to poor quality energy source, which is detrimental to the people's health and prevents the economic development of communities. Moreover, access to safe water and food security are concerns closely linked to health issues and children malnourishment. Recent climate change due to global warming has worsened the already critical situation.Electricity is well known to be an enabler of development as it allows the use of modern devices thus enabling the development of not only income-generating activities but also water pumping and food processing and conservation that can promote socioeconomic growth. However, all of this is difficult to achieve due to the lack of investors, local skills, awareness by the community, and often also government regulations.All the above mentioned barriers to the uptake of electricity in rural Kenya could be solved by the coordinated effort of government, private sector, and academia, also referred to as Triple Helix, in which each entity may partially take the other's role. This chapter discretizes the above and shows how a specific county (Marsabit) has benefited from this triple intervention. Existing government policies and actions and programs led by nongovernmental organizations (NGOs) and international agencies are reviewed, highlighting the current interconnection and gaps in promoting integrated actions toward climate change adaptation and energy access

Plants and Plant Products in Local Markets Within Benin City and Environs

AbstractThe vulnerability of agriculture systems in Africa to climate change is directly and indirectly affecting the availability and diversity of plants and plant products available in local markets. In this chapter, markets in Benin City and environs were assessed to document the availability of plants and plant products. Markets were grouped into urban, suburban, and rural with each group having four markets. Majority of the plant and plant product vendors were women and 88 plant species belonging to 42 families were found. Their scientific and common names were documented as well as the parts of the plant and associated products available in the markets. Most of the plant and plant products found in local markets belong to major plant families. Urban markets had the highest diversity of plants and plant products. Three categories of plants and plant products were documented. Around 67% of the plants and plant products were categorized as whole plant/plant parts, 28% as processed plant parts, while 5% as reprocessed plant/plant parts. It was revealed that 86% of these plants are used as foods, 11% are for medicinal purposes, while 3% is used for other purposes. About 35% of plants and plant products across the markets were fruits, which is an indication that city and environs are a rich source of fruits. The local knowledge and practices associated with the plants and plant products can contribute towards formulating a strategic response for climate change impacts on agriculture, gender, poverty, food security, and plant diversity