{"id":{"repo_id":"uwo","oai_identifier":"oai:uwo.scholaris.ca:20.500.14721/30432"},"canonical_url":"https://search.dev.ndltd.org/etd/uwo/oai:uwo.scholaris.ca:20.500.14721/30432","repository":{"repo_id":"uwo","name":"Western University","base_url":"https://uwo.scholaris.ca/server/oai/request"},"display":{"title":"An outcome-based statistical framework to select and optimize molecular clustering methods for infectious diseases","abstract":"Collecting genetic sequences from infectious pathogens allows for the construction of molecular clusters: groups of cases with genetically similar pathogen populations. These imply outbreaks, but the methods used to create them often require a threshold to qualify clusters (ie. 99% average pairwise sequence identity among cases). This project demonstrates a framework to observe the way that cluster-based outbreak detection responds to threshold selection using a variety of thresholds, three different sets of North American HIV-1 sequence data and two different methods to define clusters. This is done through cross-validation of predictive models, measuring performance through the loss of Akaike's information criterion, a metric which indicates the benefit of predictive variables given a threshold. I compare thresholds which maximize this loss between clustering methods and data sets, analyzing the optimum thresholds for clustering at each location.","abstract_html":"Collecting genetic sequences from infectious pathogens allows for the construction of molecular clusters: groups of cases with genetically similar pathogen populations. These imply outbreaks, but the methods used to create them often require a threshold to qualify clusters (ie. 99% average pairwise sequence identity among cases). This project demonstrates a framework to observe the way that cluster-based outbreak detection responds to threshold selection using a variety of thresholds, three different sets of North American HIV-1 sequence data and two different methods to define clusters. This is done through cross-validation of predictive models, measuring performance through the loss of Akaike's information criterion, a metric which indicates the benefit of predictive variables given a threshold. 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In citing or referring to this thesis, use the DOI or handle from this page instead. Sample citation: Author name, \"Thesis title.\" (Year). Western University Open Repository. https://doi.org/10.71858/123456."]},{"key":"dc:description.abstract","label":"Abstract","values":["Collecting genetic sequences from infectious pathogens allows for the construction of molecular clusters: groups of cases with genetically similar pathogen populations. These imply outbreaks, but the methods used to create them often require a threshold to qualify clusters (ie. 99% average pairwise sequence identity among cases). This project demonstrates a framework to observe the way that cluster-based outbreak detection responds to threshold selection using a variety of thresholds, three different sets of North American HIV-1 sequence data and two different methods to define clusters. This is done through cross-validation of predictive models, measuring performance through the loss of Akaike's information criterion, a metric which indicates the benefit of predictive variables given a threshold. I compare thresholds which maximize this loss between clustering methods and data sets, analyzing the optimum thresholds for clustering at each location."]},{"key":"dc:title","label":"Title","values":["An outcome-based statistical framework to select and optimize molecular clustering methods for infectious diseases"]}]}],"canonical_facts":{"dc:contributor.advisor":["Poon, Art F.Y."],"dc:creator":["Chato, Connor"],"dc:date.accessioned":["2025-07-10T18:40:51Z"],"dc:date.available":["2025-07-10T18:40:51Z"],"dc:date.issued":["2020-07-31"],"dc:description":["The thesis cover page in the PDF document includes references to Western University’s previous institutional repository platform, known as Scholarship@Western, and links to that platform (beginning with ir.lib.uwo.ca). In citing or referring to this thesis, use the DOI or handle from this page instead. Sample citation: Author name, \"Thesis title.\" (Year). Western University Open Repository. https://doi.org/10.71858/123456."],"dc:description.abstract":["Collecting genetic sequences from infectious pathogens allows for the construction of molecular clusters: groups of cases with genetically similar pathogen populations. These imply outbreaks, but the methods used to create them often require a threshold to qualify clusters (ie. 99% average pairwise sequence identity among cases). This project demonstrates a framework to observe the way that cluster-based outbreak detection responds to threshold selection using a variety of thresholds, three different sets of North American HIV-1 sequence data and two different methods to define clusters. This is done through cross-validation of predictive models, measuring performance through the loss of Akaike's information criterion, a metric which indicates the benefit of predictive variables given a threshold. I compare thresholds which maximize this loss between clustering methods and data sets, analyzing the optimum thresholds for clustering at each location."],"dc:identifier.uri":["https://hdl.handle.net/20.500.14721/30432"],"dc:language.iso":["en_ca"],"dc:publisher":["The University of Western Ontario"],"dc:subject":["Bioinformatics","infectious disease","HIV and AIDs"],"dc:title":["An outcome-based statistical framework to select and optimize molecular clustering methods for infectious diseases"],"dc:type":["thesis"],"thesis:degree_discipline":["Pathology and Laboratory Medicine"],"thesis:degree_name":["M Sc"]},"updated_at":"2026-07-27T21:56:20Z"}