Browsing by Author "Miller, Jennifer A. (Jennifer Anne)"
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Item Brazil's HIV/ AIDS model : Is it working Fortaleza? - Spatial analysis of HIV/ AIDS(2012-05) Ponte, Renata Cidrão; Miller, Jennifer A. (Jennifer Anne); Batnitzky, AdinaThe prevalence rate of the Human Immunodeficiency Virus (HIV) in Brazil has stabilized since the year 2000 at approximately 0.35 percent of the total population (600,000 people). Most researchers and political actors agree that the success in HIV management has been highly correlated with some of the policies that the Brazilian government has implemented concerning the HIV/ AIDS positive population (Levi et al 2002; Dourado 2006; Parker 2009). With worldwide recognition of this accomplishment, one must wonder why it is that the North and Northeast regions of Brazil have been experiencing trends of increasing HIV/ AIDS incidence in the past decade (Nunn et al 2009). This study concentrates on the spatial distribution of HIV incidence in the year 2000, as it uncovers how HIV distribution can be related to aspects of marginalization in the second-most populous Northeastern municipality; Fortaleza, Brazil. The central hypothesis of this research states that HIV incidence is positively correlated with rate of marginalization. Marginalization is considered as the sector of population without access to basic social services, such as education, running water, and appropriate housing. Spatial patterns of HIV and marginalization are examined and interpreted in the context of the Brazilian Model. This research suggests that although marginalization has a strong spatial pattern, HIV is not demographically or geographically discriminatory.Item How does uncertainty influence spatial projections of Anopheles presence in Kenya?(2016-05) Ames, Jillian Elizabeth; Miller, Jennifer A. (Jennifer Anne); Crews, Kelley A.; Busby, Joshua W.Species distribution models (SDM) are becoming a widely used framework for studying distribution and risk of vector-borne diseases, particularly as a consequence of climate change (Gonzalez et al. 2010; Porretta et al. 2013; Rochlin et al. 2013). Malaria has been one of the most extensively studied vector-borne diseases (Minakawa et al. 2005; Ryan et al. 2006; Afrane et al. 2008; Mboera et al. 2010; Nath et al. 2012), and SDM output has been used by policy makers and various aid organizations to design and implement preventative malaria programs for areas that have been identified as current or future high risk (Gething et al. 2012; Hongoh et al. 2012; Cianci et al. 2015). However, these maps and models are often developed by epidemiologists or other medical researchers and therefore issues related to representing or exploring the uncertainty in the results have often been ignored (Lindsay et al. 1998; Levine et al. 2004). Many sources of uncertainty in model outputs have been identified in SDM research, ranging from data type or measurement level (e.g., presence-only vs. presence-absence, abundance), to statistical method, to subjective decisions related to mapping the results (e.g., threshold selected to discretize continuous output). This studies employs SDM to project the spatial distribution of four species of Anopheles (malaria-carrying mosquitoes) in Kenya, focusing on the representation of uncertainty and its propagation associated with aspects of the modeling methods and the data used.Item Incorporating movement in species distribution models(2016-05) Holloway, Paul; Miller, Jennifer A. (Jennifer Anne); Arima, Eugenio; Di Fiore, Anthony; Keitt, Timothy; Young, Kenneth RClimate change and concomitant urbanization have led to many species shifting their geographical distribution, while other species have simply gone extinct. Understanding the current and future distributions of species is therefore a critical component of biodiversity conservation, with species distribution models (SDMs) a powerful GIScience approach increasingly used to achieve this. Movement is an ecological process that influences the distribution of all species. Broad-scale (spatially and temporally) movement includes processes like dispersal and migration that determine whether newly suitable habitats are accessible, while fine-scale movement effects resource availability, and subsequently habitat suitability. In spite of this ecological significance, movement is rarely incorporated in SDMs. An increasingly important application of SDM is to study the effects of climate change on species distributions, and while several models that incorporate species dispersal abilities have been proposed, none have been tested or compared. Past data (British birds and North American flora) were used to calibrate and extrapolate species-environment relationships to the current time-period in order to assess the accuracy of these dispersal models. Significant differences in the accuracy and area projected as present by the dispersal models were identified, and moreover, results were substantially influenced by the scale at which SDMs were calibrated. Fine-scale regular movement behaviors are another important determinant of mobile species distributions that are not currently incorporated within SDM. Spatial simulation was used to model the dynamic relationship between movement and biotic resources for oilbirds in Venezuela, in order to generate a new environmental variable for use in model calibration. The use of this layer greatly improved the accuracy and ecological realism of the SDM projection compared to other commonly applied SDM scenarios. Finally, the incorporation of movement across multiple scales has not been addressed in SDM research. Broad-scale dispersal was combined with fine-scale regular movements to predict continental changes in oilbird distribution over a decade, which improved the ecological understanding of distribution shifts and identified a number of new conceptual and methodological limitations. The incorporation of movement should now be a compulsory aspect of any study projecting the current or future distributions of species.