An outcome-based statistical framework to select and optimize molecular clustering methods for infectious diseases

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.

Name thesis:degree_name

M Sc

Discipline thesis:degree_discipline

Pathology and Laboratory Medicine

Grantor dc:publisher

The University of Western Ontario

Year dc:date.issued

2020

Author dc:creator

• Chato, Connor

Advisor dc:contributor.advisor

• Poon, Art F.Y.

• Bioinformatics
• infectious disease
• HIV and AIDs

Language dc:language.iso

en_ca

Handle dc:identifier.uri

https://hdl.handle.net/20.500.14721/30432

OAI identifier oai:identifier

oai:uwo.scholaris.ca:20.500.14721/30432