Thesis (M.Sc.)--York University, 2006. Graduate Programme in Science. / Typescript. Includes bibliographical references (leaves 62-65). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:MR29635

Vaccination against the Influenza A virus (IAV) is often an important and critical task for much of the population, as IAV causes yearly epidemics, and can cause even deadlier pandemics. Designing the vaccine requires an understanding of the current major circulating strains of Influenza, as well as an understanding of how those strains could change over time to become either less harmful or more deadly, or simply die out completely. An error in the prediction process can lead to a non-immunized population at risk of epidemics, or even a pandemic. Presented here is a posterior predictive approach to generate emerging influenza strains based on a realistic genomic model that incorporates natural features of viral evolution such as selection and recombination. Also introduced is a sequence sampling scheme to relieve the computational burden of the posterior predictive analysis by clustering sequences based on their pairwise similarity. Finally, the impact of “evolutionary accidents” that take the form of bursts of evolution and or of recombination on the predictive power of our procedure is tested. An analysis of the impact of these bursts is carried out in a retrospective study that focuses on the unexpected emergence of a new H3N2 strain in the 2007-08 influenza season. Measuring the R2 values of both pairwise and patristic distances, the model reaches a predictive power of ∼40%, but is not able to simulate the emergence of the target Brisbane/10/2007 sequence with a high probability. The inclusion of “evolutionary accidents” improved the algorithm’s ability to predict HA sequences, but the prediction power of the NA gene remained low.

Thesis (D.P.H.)--University of Michigan.

Dissertation (D.P.H.)--University of Michigan.

published_or_final_version / Community Medicine / Master / Master of Public Health

Thesis (M.S.)--University of Michigan, 1991.

Thesis (Ph. D.)--University of Hong Kong, 1998. / Includes bibliographical references (leaves 187-199).

Orientador: Hyun Mo Yang / Dissertação (mestrado profissional) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica / Made available in DSpace on 2018-08-15T15:17:49Z (GMT). No. of bitstreams: 1 Caetano_MarcoTulioPeres_M.pdf: 770219 bytes, checksum: 1bbe772b6e5adcdbba5e870758a93226 (MD5) Previous issue date: 2010 / Resumo: Este trabalho tem por finalidade modelar a Influenza A(H1N1). Utilizando equações diferenciais e fundamentando-se nos aspectos biológicos do processo contagioso, obtemos um sistema dinâmico. A evolução da doença dentro de uma população considerada constante é analisada através dos pontos de equilíbrio trivial e não trivial do sistema e suas estabilidades. Neste contexto devemos destacar o conceito de número de reprodutibilidade basal (R0), que é o número de novos casos de infecção gerados por um indivíduo infectado quando introduzido em uma população totalmente suscetível. Se esse número for menor que um a doença será erradicada e se for maior que um a doença será endêmica / Abstract: This study aims to model the Influenza A (H1N1). Using differential equations, and based on the biological aspects of the infectious process, we obtain a dynamic system. The evolution of the disease within a population, considered constant, was examined through the trivial and nontrivial equilibrium points of the system, and their stabilities. In this context we emphasize the concept of basic reproduction number (R0), which is the number of new cases of infection generated by one infected individual when introduced into a totally susceptible population. When this number is less than one the disease should be eradicated, but the disease will be able to spread in a population if this number is greater than one / Mestrado / Biomatematica / Mestre em Matemática

published_or_final_version / abstract / Microbiology / Doctoral / Doctor of Philosophy

published_or_final_version / Biological Sciences / Doctoral / Doctor of Philosophy