abstract: Accounting for over a third of all emerging and re-emerging infections, viruses represent a major public health threat, which researchers and epidemiologists across the world have been attempting to contain for decades. Recently, genomics-based surveillance of viruses through methods such as virus phylogeography has grown into a popular tool for infectious disease monitoring. When conducting such surveillance studies, researchers need to manually retrieve geographic metadata denoting the location of infected host (LOIH) of viruses from public sequence databases such as GenBank and any publication related to their study. The large volume of semi-structured and unstructured information that must be reviewed for this task, along with the ambiguity of geographic locations, make it especially challenging. Prior work has demonstrated that the majority of GenBank records lack sufficient geographic granularity concerning the LOIH of viruses. As a result, reviewing full-text publications is often necessary for conducting in-depth analysis of virus migration, which can be a very time-consuming process. Moreover, integrating geographic metadata pertaining to the LOIH of viruses from different sources, including different fields in GenBank records as well as full-text publications, and normalizing the integrated metadata to unique identifiers for subsequent analysis, are also challenging tasks, often requiring expert domain knowledge. Therefore, automated information extraction (IE) methods could help significantly accelerate this process, positively impacting public health research. However, very few research studies have attempted the use of IE methods in this domain. This work explores the use of novel knowledge-driven geographic IE heuristics for extracting, integrating, and normalizing the LOIH of viruses based on information available in GenBank and related publications; when evaluated on manually annotated test sets, the methods were found to have a high accuracy and shown to be adequate for addressing this challenging problem. It also presents GeoBoost, a pioneering software system for georeferencing GenBank records, as well as a large-scale database containing over two million virus GenBank records georeferenced using the algorithms introduced here. The methods, database and software developed here could help support diverse public health domains focusing on sequence-informed virus surveillance, thereby enhancing existing platforms for controlling and containing disease outbreaks. / Dissertation/Thesis / Doctoral Dissertation Biomedical Informatics 2019

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Indiana University-Purdue University Indianapolis (IUPUI) / While optimization techniques have been studied since 300 B.C. when Euclid first considered the minimal distance between a point and a line, it wasn’t until 1966 that location optimization was first applied to a problem in healthcare. Location optimization techniques are capable of increasing efficiency and equity in the placement of many types of services, including those within the healthcare industry, thus enhancing quality of life. Medical imaging is a healthcare service which helps to determine medical diagnoses in acute and preventive care settings. It provides physicians with information guiding treatment and returning a patient back to optimal health. In this study, a retrospective analysis of the locations of current medical imaging centers in central Indiana is performed, and alternate placement as determined using optimization techniques is considered and compared. This study focuses on reducing the drive time experienced by the population within the study area to their nearest imaging facility. Location optimization models such as the P-Median model, the Maximum Covering model, and Clustering and Partitioning are often used in the field of operations research to solve location problems, but are lesser known within the discipline of Geographic Information Science. This study was intended to demonstrate the capabilities of these powerful algorithms and to increase understanding of how they may be applied to problems within healthcare. While the P-Median model is effective at reducing the overall drive time for a given network set, individuals within the network may experience lengthy drive times. The results further indicate that while the Maximum Covering model is more equitable than the P-Median model, it produces large sets of assigned individuals overwhelming the capacity of one imaging center. Finally, the Clustering and Partitioning method is effective at limiting the number of individuals assigned to a given imaging center, but it does not provide information regarding average drive time for those individuals. In the end, it is determined that a capacitated Maximal Covering model would be the preferred method for solving this particular location problem.

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