published_or_final_version / Biological Sciences / Doctoral / Doctor of Philosophy

With the threat of avian influenza, influenza laboratory testing and surveillance capacity has increased globally. Data from global surveillance activities have been used to identify circulating influenza strains for vaccine policy decisions, and have provided evidence of influenza disease among various populations. A recent meta-analysis, which includes findings from these surveillance efforts, has shown that influenza contributes to 10% of pediatric respiratory hospitalizations. Although statistical models indicate a high burden of influenza-associated morbidity among older adults and pandemic studies reveal an increase in hospitalizations among young adults, the global burden of seasonal influenza among adults remains unknown. In order to estimate the global burden of seasonal influenza among adult respiratory hospitalizations, we conducted a systematic review of the published literature, and identified 48 eligible articles published between January 1996 and June 2012 that met our inclusion criteria. We combined these published datasets with 29 eligible, unique datasets from year-round, influenza hospital-based surveillance. These combined data covered 50 countries with varying income and vaccine policies. Extracting numbers tested and positive for influenza, we calculated crude median positive proportions and evaluated potential differences in crude proportions among variables using Kruskal-Wallis non-parametric tests. We observed differences by data source and country development status when we included the 2009 pandemic year. With the exclusion of the 2009 pandemic year, we then generated adjusted pooled estimates using the log binomial model. We found 11% of cases from adult respiratory hospitalizations worldwide were laboratory-confirmed for influenza. This pooled estimate was independent of age but increased as country development or income level decreased. Our findings suggest that influenza is an important contributor to severe acute respiratory illness among both young and older adult populations. For countries without reliable influenza data, we provide an estimate that they may use in planning and allocating resources for the control and prevention of influenza.

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 / Community Medicine / Doctoral / Doctor of Philosophy

published_or_final_version / Nursing Studies / Master / Master of Nursing in Advanced Practice

The last two decades have seen several large-scale epidemics of international impact, including human, animal and plant epidemics. Policy makers face health challenges that require epidemic predictions based on limited information. There is therefore a pressing need to construct models that allow us to frame all available information to predict an emerging outbreak and to control it in a timely manner. The aim of this thesis is to develop an early-warning modelling approach that can predict emerging disease outbreaks. Based on Bayesian techniques ideally suited to combine information from different sources into a single modelling and estimation framework, I developed a suite of approaches to epidemiological data that can deal with data from different sources and of varying quality. The SEIR model, particle filter algorithm and a number of influenza-related datasets were utilised to examine various models and methodologies to predict influenza outbreaks. The data included a combination of consultations and diagnosed influenza-like illness (ILI) cases for five influenza seasons. I showed that for the pandemic season, different proxies lead to similar behaviour of the effective reproduction number. For influenza datasets, there exists a strong relationship between consultations and diagnosed datasets, especially when considering time-dependent models. Individual parameters for different influenza seasons provided similar values, thereby offering an opportunity to utilise such information in future outbreaks. Moreover, my findings showed that when the temperature drops below 14°C, this triggers the first substantial rise in the number of ILI cases, highlighting that temperature data is an important signal to trigger the start of the influenza epidemic. Further probing was carried out among Maltese citizens and estimates on the under-reporting rate of the seasonal influenza were established. Based on these findings, a new epidemiological model and framework were developed, providing accurate real-time forecasts with a clear early warning signal to the influenza outbreak. This research utilised a combination of novel data sources to predict influenza outbreaks. Such information is beneficial for health authorities to plan health strategies and control epidemics.

Cette thèse évalue le risque d’émergence d’un pathogène via l’avifaune sauvage dans une région indemne en combinant deux approches :(1) L’étude de pathogènes partageant des caractéristiques éco-épidémiologiques communes avec le pathogène émergeant ;et (2) L’utilisation de données écologiques disponibles dans la région indemne.<p>\ / Doctorat en Sciences agronomiques et ingénierie biologique / info:eu-repo/semantics/nonPublished