Browsing by Author "Keller, Kayleigh, committee member"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item Open Access Integrated statistical models in ecology(Colorado State University. Libraries, 2023) Van Ee, Justin, author; Hooten, Mevin, advisor; Koslovsky, Matthew, advisor; Keller, Kayleigh, committee member; Kaplan, Andee, committee member; Bailey, Larissa, committee memberThe number of endangered and vulnerable species continues to grow globally as a result of habitat destruction, overharvesting, invasive species, and climate change. Understanding the drivers of population decline is pivotal for informing species conservation. Many datasets collected are restricted to a limited portion of the species range, may not include observations of other organisms in the community, or lack temporal breadth. When analyzed independently, these datasets often overlook drivers of population decline, muddle community responses to ecological threats, and poorly predict population trajectories. Over the last decade, thanks to efforts like The Long Term Ecological Research Network and National Ecological Observatory Network, citizen science surveys, and technological advances, ecological datasets that provide insights about collections of organisms or multiple characteristics of the same organism have become prevalent. The conglomerate of datasets has the potential to provide novel insights, improve predictive performance, and disentangle the contributions of confounded factors, but specifying joint models that assimilate all the available data sources is both intellectually daunting and computationally prohibitive. I develop methodology for specifying computationally efficient integrated models. I discuss datasets frequently collected in ecology, objectives common to many analyses, and the methodological challenges associated with specifying joint models in these contexts. I introduce a suite of model building and computational techniques I used to facilitate inference in three applied analyses of ecological data. In a case study of the joint mammalian response to the bark beetle epidemic in Colorado, I describe a restricted regression approach to deconfounding the effects of environmental factors and community structure on species distributions. I highlight that fitting certain joint species distribution models in a restricted parameterization improves sampling efficiency. To improve abundance estimates for a federally protected species, I specify an integrated model for analyzing independent aerial and ground surveys. I use a Markov melding approach to facilitate posterior inference and construct the joint distribution implied by the prior information, assumptions, and data expressed across a chain of submodels. I extend the integrated model by assimilating additional demographic surveys of the species that allow abundance estimates to be linked to annual variability in population vital rates. To reduce computation time, both models are fit using a multi-stage Markov chain Monte Carlo algorithm with parallelization. In each applied analysis, I uncover associations that would have been overlooked had the datasets been analyzed independently and improve predictive performance relative to models fit to individual datasets.Item Open Access Statistical models for COVID-19 infection fatality rates and diagnostic test data(Colorado State University. Libraries, 2023) Pugh, Sierra, author; Wilson, Ander, advisor; Fosdick, Bailey K., advisor; Keller, Kayleigh, committee member; Meyer, Mary, committee member; Gutilla, Molly, committee memberThe COVID-19 pandemic has had devastating impacts worldwide. Early in the pandemic, little was known about the emerging disease. To inform policy, it was essential to develop data science tools to inform public health policy and interventions. We developed methods to fill three gaps in the literature. A first key task for scientists at the start of the pandemic was to develop diagnostic tests to classify an individual's disease status as positive or negative and to estimate community prevalence. Researchers rapidly developed diagnostic tests, yet there was a lack of guidance on how to select a cutoff to classify positive and negative test results for COVID-19 antibody tests developed with limited numbers of controls with known disease status. We propose selecting a cutoff using extreme value theory and compared this method to existing methods through a data analysis and simulation study. Second, there lacked a cohesive method for estimating the infection fatality rate (IFR) of COVID-19 that fully accounted for uncertainty in the fatality data, seroprevalence study data, and antibody test characteristics. We developed a Bayesian model to jointly model these data to fully account for the many sources of uncertainty. A third challenge is providing information that can be used to compare seroprevalence and IFR across locations to best allocate resources and target public health interventions. It is particularly important to account for differences in age-distributions when comparing across locations as age is a well-established risk factor for COVID-19 mortality. There is a lack of methods for estimating the seroprevalence and IFR as continuous functions of age, while adequately accounting for uncertainty. We present a Bayesian hierarchical model that jointly estimates seroprevalence and IFR as continuous functions of age, sharing information across locations to improve identifiability. We use this model to estimate seroprevalence and IFR in 26 developing country locations.Item Embargo The impact of control on national-scale livestock disease outbreaks in the United States(Colorado State University. Libraries, 2023) Smith, Samuel M., author; Webb, Colleen T., advisor; Beck-Johnson, Lindsay M., committee member; Keller, Kayleigh, committee memberOutbreaks of livestock diseases, like foot-and-mouth disease (FMD) and bovine tuberculosis (bTB), pose a significant economic threat to the United States livestock industry. Significant interest then lies in developing strategies to mitigate the impact of an outbreak should they occur. This thesis explores the effect of control interventions on outbreaks of FMD and bTB in the U.S. In chapter one, I weigh trade-offs associated with delaying the implementation of control on the economic impact of controlling an FMD outbreak in the U.S. This study aimed to understand whether control policies that adopt a conservative initial approach, but may be updated as an outbreak progresses, can reduce socioeconomic harm while achieving desired outbreak outcomes. I find that delaying the implementation of all available control interventions early on in an outbreak does not reduce the cost of small outbreaks and exacerbates the largest outbreaks, suggesting that the potential benefits of this type of adaptive response may be out weighted by the risk of allowing a large outbreak to become worse. Next, I investigate how the culling of infected cattle premises, diagnostic testing, and traceback investigations impact the size of bTB outbreaks. Results from this study show improvements to traceback investigations result in the largest decreases in bTB outbreak size, which suggests that improving the identification of premises via traceback investigations is more important than increasing antemortem diagnostic sensitivity. Although this thesis focuses on the control of livestock disease, we can abstract several broader principles that contribute to ecology and epidemiology's understanding of disease dynamics. Both chapters demonstrate the importance of a population's underlying demography to determining an outbreak's overall trajectory as well as minimizing the time until detection of an infection and the time until control is implemented.