Epidemiology and phylogenetic analysis of West Nile virus in Ontario, Canada, 2002-2003 /

Since the discovery of West Nile (WN) virus in the Western Hemisphere many surveillance programs have been implemented to monitor the epidemiology and genetic variation of WN virus in North America. This project was based on the WN virus Adult Mosquito Identification and Diagnostic Program conducted at Brock University for Ontario, Canada, during the 2002 and 2003 transmission seasons. There are three sections to this thesis. The first section investigated which mosquito species carry WN virus in Ontario, Canada throughout the 2002-2003 transmission seasons. It was found that from the 2002 data, eight mosquito species were detected with WN virus (Aedes vexans, Anopheles punctipennis, Coquilleltidia perlurbans, Culex salinarius, Cx. pipiens, Cx. resluans, Ochlerolalus Irivillalus and Och. Iriserialus) and 7.19% of the total mosquito pools tested were found to be WN virus positive (129 positive poolsll, 793 total pools tested). In 2003, WN virus was detected in only five mosquito species (Ae. vexans, Cx. salinarius, Och. Iriserialus, Cx. pipiens and Cx. resluans) and 1.42% of the total mosquito pools tested were WN virus positive (101 positive poolsl7,1 01 total pools tested). WN virus positive mosquito pools were detected 3-4 weeks earlier in 2002 compared to 2003 data. The second section investigated the actual infection rate (IR) of clearly identified Cx. pipiens and Cx. resluans from the 2002 outbreak. It was found that significantly more ex. resluans were infected with WN virus compared to ex. pipiens. The third section investigated the degree of variability of the WN virus genome. A 879 nucleotide section of the WN virus genome was amplified from 21 American Crows and 20 adult female mosquitoes from Ontario, Canada, and compared to the homologous region of the original New York 1999 Chilean Flamingo sequence (NY99FL). Seventy-two nucleotides from Ontario WN virus sequences showed variability compared to NY99FL with 10 synapotypic changes. Phylogenetic analysis revealed a close relationship between Ontario and US WN virus sequences.

Automatic Inference of Graph Models for Complex Networks with Genetic Programming

Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.