Social and Behavioral Sciences Commons^™

Emergence Of Covid-19 And Patterns Of Early Transmission In An Appalachian Sub-Region, Abbey K. Mann, Timothy A. Joyner, Ingrid E. Luffman, Megan Quinn, William Tollefson, Ashley Frazier

Journal of Appalachian Health

Background: In mid-March 2020, very few cases of COVID-19 had been confirmed in the Central Blue Ridge Region, an area in Appalachia that includes 47 jurisdictions across northeast Tennessee, western North Carolina, and southwest Virginia. Authors described the emergence of cases and outbreaks in the region between March 18 and June 11, 2020.

Methods: Data were collected from the health department websites of Tennessee, North Carolina, and Virginia beginning in mid-March for an ongoing set of COVID-19 monitoring projects, including a newsletter for local healthcare providers and a Geographic Information Systems (GIS) dashboard. In Fall 2020, using these databases, authors …

Improving Access To Treatment For Opioid Use Disorder In High-Need Areas: The Role Of Hrsa Health Centers, Michael Topmiller, Jennifer Rankin, Jessica L. Mccann, Jene Grandmont, David Grolling, Mark Carrozza, Hank Hoang, Josh Bolton, Alek Sripipatana

Journal of Appalachian Health

Introduction: Despite the opioid epidemic adversely affecting areas across the U.S. for more than two decades and increasing evidence that medication-assisted treatment (MAT) is effective for patients with opioid use disorder (OUD), access to treatment is still limited. The limited access to treatment holds true in the Appalachia region despite being disproportionately affected by the crisis, particularly in rural, central Appalachia.

Purpose: This research identifies opportunities for health centers located in high-need areas based on drug poisoning mortality to better meet MAT care gaps. We also provide an in-depth look at health center MAT capacity relative to need …

Leveraging Overhead Imagery For Localization, Mapping, And Understanding, Scott Workman

Theses and Dissertations--Computer Science

Ground-level and overhead images provide complementary viewpoints of the world. This thesis proposes methods which leverage dense overhead imagery, in addition to sparsely distributed ground-level imagery, to advance traditional computer vision problems, such as ground-level image localization and fine-grained urban mapping. Our work focuses on three primary research areas: learning a joint feature representation between ground-level and overhead imagery to enable direct comparison for the task of image geolocalization, incorporating unlabeled overhead images by inferring labels from nearby ground-level images to improve image-driven mapping, and fusing ground-level imagery with overhead imagery to enhance understanding. The ultimate contribution of this thesis …