Department of Statistics

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Browsing Department of Statistics by Department "Geosciences"

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    Development and implementation of a scalable and versatile test for COVID-19 diagnostics in rural communities
    Ceci, Alessandro; Muñoz-Ballester, Carmen; Tegge, Allison N.; Brown, Katherine L.; Umans, Robyn A.; Michel, F. Marc; Patel, Dipankumar; Tewari, Bhanu P.; Martin, James E.; Alcoreza, Oscar Jr.; Maynard, Thomas M.; Martinez-Martinez, Daniel; Bordwine, Paige; Bissell, Noelle; Friedlander, Michael J.; Sontheimer, Harald; Finkielstein, Carla V. (Nature Publishing Group, 2021-07-20)
    Rapid and widespread testing of severe acute respiratory coronavirus 2 (SARS-CoV-2) is essential for an effective public health response aimed at containing and mitigating the coronavirus disease 2019 (COVID-19) pandemic. Successful health policy implementation relies on early identification of infected individuals and extensive contact tracing. However, rural communities, where resources for testing are sparse or simply absent, face distinctive challenges to achieving this success. Accordingly, we report the development of an academic, public land grant University laboratory-based detection assay for the identification of SARS-CoV-2 in samples from various clinical specimens that can be readily deployed in areas where access to testing is limited. The test, which is a quantitative reverse transcription polymerase chain reaction (RT-qPCR)-based procedure, was validated on samples provided by the state laboratory and submitted for FDA Emergency Use Authorization. Our test exhibits comparable sensitivity and exceeds specificity and inclusivity values compared to other molecular assays. Additionally, this test can be re-configured to meet supply chain shortages, modified for scale up demands, and is amenable to several clinical specimens. Test development also involved 3D engineering critical supplies and formulating a stable collection media that allowed samples to be transported for hours over a dispersed rural region without the need for a cold-chain. These two elements that were critical when shortages impacted testing and when personnel needed to reach areas that were geographically isolated from the testing center. Overall, using a robust, easy-to-adapt methodology, we show that an academic laboratory can supplement COVID-19 testing needs and help local health departments assess and manage outbreaks. This additional testing capacity is particularly germane for smaller cities and rural regions that would otherwise be unable to meet the testing demand.
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    Evaluating Geologic Sources of Arsenic in Well Water in Virginia (USA)
    VanDerwerker, Tiffany; Zhang, Lin; Ling, Erin; Benham, Brian L.; Schreiber, Madeline E. (MDPI, 2018-04-18)
    We investigated if geologic factors are linked to elevated arsenic (As) concentrations above 5 μg/L in well water in the state of Virginia, USA. Using geologic unit data mapped within GIS and two datasets of measured As concentrations in well water (one from public wells, the other from private wells), we evaluated occurrences of elevated As (above 5 μg/L) based on geologic unit. We also constructed a logistic regression model to examine statistical relationships between elevated As and geologic units. Two geologic units, including Triassic-aged sedimentary rocks and Triassic-Jurassic intrusives of the Culpeper Basin in north-central Virginia, had higher occurrences of elevated As in well water than other geologic units in Virginia. Model results support these patterns, showing a higher probability for As occurrence above 5 μg/L in well water in these two units. Due to the lack of observations (<5%) having elevated As concentrations in our data set, our model cannot be used to predict As concentrations in other parts of the state. However, our results are useful for identifying areas of Virginia, defined by underlying geology, that are more likely to have elevated As concentrations in well water. Due to the ease of obtaining publicly available data and the accessibility of GIS, this study approach can be applied to other areas with existing datasets of As concentrations in well water and accessible data on geology.