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This study highlights the significance of zoonotic diseases, which make up almost 60% of infectious diseases in humans, and their origin from animals. Among mammalian viruses, primates, bats, and rodents have been identified as high-risk carriers. Within the rodent family Cricetidae, the species complex of Peromyscus eremicus, Peromyscus californicus, Peromyscus

This study highlights the significance of zoonotic diseases, which make up almost 60% of infectious diseases in humans, and their origin from animals. Among mammalian viruses, primates, bats, and rodents have been identified as high-risk carriers. Within the rodent family Cricetidae, the species complex of Peromyscus eremicus, Peromyscus californicus, Peromyscus fraterculus, and Osgoodomys banderanus have been found to play a crucial role in disease transmission. These four species are phylogenetically related and share similar physical appearances and ecological niches. They have been identified as carriers of several zoonotic diseases, including Hantavirus, Arenavirus, Yersinia pestis, and Flavivirus, with a history of spread to humans. Despite their implications for public health, many of these species remain understudied. Thus, this study aims to provide a systematic review of the existing literature on these four species to summarize the findings on virus prevalence and distribution. The review shows that sampling efforts have been uneven and recent efforts have been lacking, with potential undiscovered zoonotic diseases. The concentration of sampling efforts in California and gaps in the literature are concerning, especially with changing agriculture and climate change potentially affecting rodent communities.

ContributorsTariq, Muhamamad (Author) / Sterner, Beckett (Thesis director) / Upham, Nate (Committee member) / Barrett, The Honors College (Contributor) / Dean, W.P. Carey School of Business (Contributor) / School of Life Sciences (Contributor)
Created2023-05
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Statistical model selection using the Akaike Information Criterion (AIC) and similar criteria is a useful tool for comparing multiple and non-nested models without the specification of a null model, which has made it increasingly popular in the natural and social sciences. De- spite their common usage, model selection methods are

Statistical model selection using the Akaike Information Criterion (AIC) and similar criteria is a useful tool for comparing multiple and non-nested models without the specification of a null model, which has made it increasingly popular in the natural and social sciences. De- spite their common usage, model selection methods are not driven by a notion of statistical confidence, so their results entail an unknown de- gree of uncertainty. This paper introduces a general framework which extends notions of Type-I and Type-II error to model selection. A theo- retical method for controlling Type-I error using Difference of Goodness of Fit (DGOF) distributions is given, along with a bootstrap approach that approximates the procedure. Results are presented for simulated experiments using normal distributions, random walk models, nested linear regression, and nonnested regression including nonlinear mod- els. Tests are performed using an R package developed by the author which will be made publicly available on journal publication of research results.
ContributorsCullan, Michael J (Author) / Sterner, Beckett (Thesis advisor) / Fricks, John (Committee member) / Kao, Ming-Hung (Committee member) / Arizona State University (Publisher)
Created2018