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To investigate a potential risk for multiple sclerosis (MS) after vaccination with Arepanrix, the GlaxoSmithKline AS03-adjuvanted influenza A(H1N1)pdm09 vaccine, we used the provincewide immunization registry for Manitoba, Canada, to match 341,347 persons vaccinated during the 2009 pandemic to 485,941 unvaccinated persons on age, sex, address, and a propensity score measuring the probability of vaccination. We used a previously validated algorithm to identify MS cases from provincial hospital, physician, and prescription drug claims databases. After 12 months of follow-up, the age-adjusted incidence rate of MS was 17.7 cases per 100,000 person-years in the Arepanrix cohort and 24.2 per 100,000 in the unvaccinated cohort. The corresponding adjusted hazard ratio was 0.9. We observed similar patterns when we measured incidence over the entire follow-up period. The AS03 adjuvant, a candidate for inclusion in future pandemic vaccines, does not appear to increase the short-term risk for MS when included in influenza vaccines.

Invasive pneumococcal disease (IPD) remains a significant public health problem in Manitoba, Canada although publically-funded pneumococcal conjugate (PCV7 and PCV13) and polysaccharide (PPV23) vaccination programs exist. We analyzed routine surveillance and administrative health data to examine trends in IPD rates as these vaccines were introduced. Data on all individuals with a laboratory-confirmed diagnosis of IPD between 2001 and 2014 were obtained from the provincial Communicable Diseases Surveillance database and linked with Manitoba's provincial immunization registry and physician and hospital databases. We calculated IPD incidence rates overall, by serotype and for different population subgroups defined by socio-demographic and clinical (e.g., chronic diseases, immune status) characteristics. Annual IPD incidence (95%CI) was 8.6 (8.2–9.1)/100,000 people during the study period (n = 1092), and rates were higher in recent years and in regions with predominately indigenous populations. Reduction in the incidence of serotypes included in PCV7 have been offset by rising rates of PCV13-only serotypes in children, and more recently by rising rates of PPV-only serotypes and non-vaccine serotypes among young children and older adults (≥ 65 years). Rates were 3 times higher in those with a chronic disease and highest (> 175-fold) among alcoholics, organ-transplant, and chronic kidney failure patients. The case fatality rate was 12.0% within 30 d of diagnosis. Despite the introduction of several vaccination programs, overall rates of IPD have not declined in Manitoba in the last decade, due to increase in incidence of non-PCV7 serotypes. A disproportionately high burden of disease impacts indigenous communities and people with chronic disease.

Objectives. We estimated age-standardized ratios of infection and hospitalization among Canadian First Nations (FN) populations and compared their distributions with those estimated for non-FN populations in Manitoba, Canada. Methods. For the spring and fall 2009 waves of the H1N1 pandemic, we obtained daily numbers of laboratory-confirmed and hospitalized cases of H1N1 infection, stratified by 5-year age groups and FN status. We calculated age-standardized ratios with confidence intervals for each wave and compared ratios between age groups in each ethnic group and between the 2 waves for FN and non-FN populations. Results. Incidence and hospitalization ratios in all FN age groups during the first wave were significantly higher than those in non-FN age groups (P Conclusions. Incidence and hospitalization ratios in FN populations were higher than or equivalent to ratios in non-FN populations. Our findings support the need to develop targeted prevention and control strategies specifically for vulnerable FN and remote communities.

Background: An increased incidence of hospital admissions coded as acute disseminated encephalomyelitis (ADEM) was noted in Winnipeg, Manitoba, Canada, during the second wave of the influenza pandemic from October 2009 to March 2010. However, it was not clear whether this was due to heightened awareness of potential neurological complications of influenza or influenza vaccination or an actual increase in the number of cases. Methods: We extracted data from the charts of 139 patients hospitalized with an International Classification of Diseases-10 discharge code indicating ADEM (G04.0) or unspecified noninfectious encephalitis or myelitis (G04.8, G04.9) between January 2006 and December 2012. Clinical and laboratory data were reviewed by a neurologist, and diagnoses were determined using the Brighton criteria. Results: Over the entire study period, there were 22 cases of ADEM. During the peak pandemic period (April-December 2009), seven patients were hospitalized with ADEM, corresponding to a rate of 7.8/million/year; 4.7 (95% confidence interval: 1.9-11.4) times higher than the rate before or after the pandemic period. Only one patient with ADEM had received the monovalent A(H1N1)pdm09 vaccine within 12 weeks of hospitalization. Conclusions: We have found an increased incidence of ADEM during the pandemic period that may be related, at least in part, to the increased incidence of influenza during that period. However, there was no temporal relationship with the administration of A(H1N1)pdm09 or seasonal influenza vaccines. Our study provides reassurance that use of these vaccines was not associated with increased risk of ADEM.

A utilização de redes complexas para a descriçãoi de diversos sistemas naturais e artificias,compreendidos nas mais diversas áreas do conhecimento humano, tem se mostrado uma abordagem poderosa para a redução da complexidade inerente a tais sistemas. Em muitos casos, tal complexidade resulta do número de componentes envolvidos e de suas intrincadas relações. Uma forma de reduzir a complexidade associada a tais sistemas, consiste em identificar e agrupar componentes que possuam características similares. Sendo assim, desenvolvemos nesta tese métodos de identificação de comunidades em redes complexas. Tais métodos se baseiam na ideia de que comunidades surgem quando grupos de vértices possuem um número mais elevado de conexões entre os vértices do mesmo grupo do que com vértices externos à este grupo. Além disso, utilizamos a função modularidade como função objetivo e como forma de avaliação e comparação dos resultados obtidos nesta tese com resultados previamente reportados na literatura. Uma vez estabelecido um método de identificação de comunidades, utilizamos a abordagem de redes complexas para a determinação de domínios estruturais de proteínas. Para tal, criamos redes de contato entre os aminoácidos de uma proteí?na buscando representar apenas as ligações relevantes do ponto de vista topológico. Por meio destas representações, aplicamos os métodos de identificação de comunidades desenvolvidos nesta tese, no intuito de identificar domínios estruturais de cadeias proteicas. Por fim, desenvolvemos um método específico para a identificação de domínios em proteínas com dois domínios sequencias, concluindo desta forma, os objetivos propostos nesta tese. The use of complex networks for the representation of various natural and artificial sys- tems in the most diverse fields of human knowledge, has proven to be a powerful approach for the reduction of the complexity in the study of such systems. In many cases, this complexity emerges from the number of components of the system and from the intricate relationship between them. A reduction in this complexity is made possible by the iden- tification and grouping of the components of the system with similar characteristics. In this way, we developed in this thesis, methods for community identification in complex networks. Such methods are based on the notion that communities arise when groups of vertices are more densely connected with vertices of their same group, than with ver- tices belonging to other groups in the network. Moreover, the modularity function has been used as an objective function, and as a score for the evaluation and comparison of the results obtained in this thesis with the results reported in the literature of complex networks. Upon the establishment of a method for community detection, we used the framework of complex networks to the determination of structural protein domains. The- refore, we have created contact networks of amino acids of protein chains, focusing on the representation of only the most relevant interactions between them, from a topological point of view. We have applied to these networks the methods for community identi- fication developed in this thesis, aiming to identify the structural domains of proteins. Finally, we have developed a specific method for the identification of protein domains in protein chains with two sequential domains, concluding in this way, the objectives proposed in this thesis.

The role of demographic variables in disease spread in remote and isolated communities is poorly understood. We developed an agent-based model of a small indigenous community to qualitatively study the impact of pre-existing immunity in both young and elderly populations. We observed that in crowded living conditions, the age distribution of the population is a critical factor influencing epidemic spread. As the average age of the population increases, the effect of the pre-existing immunity in older individuals becomes more pronounced in decreasing disease incidence, even when pre-existing immunity levels in young individuals are low. However, in a non-crowded setting with relatively low average persons-per-household, the pre-existing immunity levels of young individuals remains a determining factor, regardless of the age distribution of the population. We suggest that for optimizing public health policies, social and demographic complexities of the remote and vulnerable communities should be carefully evaluated in modeling intervention strategies.

From the Black Death of 1347–1350 (Murray, 2007) and the Spanish influenza pandemic of 1918–1919 (Taubenberger & Morens, 2006), to the more recent 2003 SARS outbreak (Lingappa et al., 2004) and the 2009 influenza pandemic (Moghadas et al., 2009), as well as countless outbreaks of childhood infections, infectious diseases have been the bane of humanity throughout its existence causing significant morbidity, mortality, and socioeconomic upheaval. Advanced modelling technologies, which incorporate the most current knowledge of virology, immunology, epidemiology, vaccines, antiviral drugs, and public health, have recently come to the fore in identifying effective disease mitigation strategies, and are being increasingly used by public health experts in the study of both epidemiology and pathogenesis. Tracing its historical roots from the pioneering work of Daniel Bernoulli on smallpox (Bernoulli, 1760) to the classical compartmental approach of Kermack and McKendrick (Kermack & McKendrick, 1927), modelling has evolved to deal with data that is more heterogeneous, less coarse (based at a community or individual level), and more complex (joint spatial, temporal and behavioural interactions). This evolution is typified by the agent-based model (ABM) paradigm, lattice-distributed collections of autonomous decision-making entities (agents), the interactions of which unveil the dynamics and emergent properties of the infectious disease outbreak under investigation. The flexibility of ABMs permits an effective representation of the complementary interactions between individuals characterized by localized properties and populations at a global level. However, with flexibility comes complexity; hence, the software implementation of an ABM demands more stringent software design requirements than conventional (and simpler) models of the spread and control of infectious diseases, especially with respect to outcome reproducibility, error detection and system management. Outcome reproducibility is a challenge because emergent properties are not analytically tractable, which is further exacerbated by subtle and difficult to detect errors in algorithm logic and software design. System management of software simulating populations/individuals and biological /physical interactions is a serious challenge, as the implementation will involve distributed (parallelized), non-linear, complex, and multiple processes operating in concert. Given these

SCTE intends to provide a simple, yet powerful, framework for building data acquisition and equipment control systems for experimental Physics, and correlated areas. Via its SCTE::Instrument module, RS-232, USB, and LAN buses are supported, and the intricacies of hardware communication are encapsulated underneath an object oriented abstraction layer. Written in Perl, and using the SCPI protocol, enabled instruments can be easily programmed to perform a wide variety of tasks. While this work presents general aspects of the development of data acquisition systems using the SCTE framework, it is illustrated by particular applications designed for the calibration of several in-house developed devices for power measurement in the tokamak TCABR Alfv\'en Waves Excitement System., Comment: 15 pages, 6 figures. Related software application, SCTE, registered under GPL v3 license, available at http://code.google.com/p/scte

Objectives During the first wave of the 2009 influenza pH1N1, disease burden was distributed in a geographically heterogeneous fashion. It was particularly high in some remote and isolated Canadian communities when compared with urban centres. We sought to estimate the transmissibility (the basic reproduction number) of pH1N1 strain in some remote and isolated Canadian communities. Design A discrete time susceptible-exposed-infected transmission model was fit to infection curves simulated from laboratory-confirmed case counts for pH1N1 on each day. The sampling from Poisson distribution was used to estimate the basic reproduction number, R0, of pH1N1 during the spring wave for five different communities in Manitoba and Nunavut, Canada, where remote and isolated communities experienced a high incidence of infection, and high rates of hospitalisation and intensive care unit admission. Setting Remote and isolated communities in Northern Manitoba, Nunavut, and the largest urban centre (Winnipeg) in the province of Manitoba, Canada. Results Using published values of the exposed and infectious periods specific to H1N1 infection, corresponding to the average generation time of 2.78 days, we estimated a mean value of 2.26 for R0 (95% CI 1.57 to 3.75) in a community located in northern Manitoba. Estimates of R0 for other communities in Nunavut varied considerably with higher mean values of 3.91 (95% CI 3.08 to 4.87); 2.03 (95% CI 1.50 to 3.19); and 2.45 (95% CI 1.68 to 3.44). We estimated a lower mean value of 1.57 (95% CI 1.35 to 1.87) for R0 in the Winnipeg health region, as the largest urban centre in Manitoba. Conclusions Influenza pH1N1 appears to have been far more transmissible in rural and isolated Canadian communities than other large urban areas. The differential severity of the pandemic in these regions may be explained partly by differential transmissibility, and suggests the need for more nuanced, targeted or population-specific control strategies in Canada.

Background The prevalence and severity of the 2009 H1N1 pandemic appeared to vary significantly across populations and geographic regions. We sought to investigate the variability in transmissibility of H1N1 pandemic in different health regions (including urban centres and remote, isolated communities) in the province of Manitoba, Canada. Methods The Richards model was used to fit to the daily number of laboratory-confirmed cases and estimate transmissibility (referred to as the basic reproduction number, R0), doubling times, and turning points of outbreaks in both spring and fall waves of the H1N1 pandemic in several health regions. Results We observed considerable variation in R0 estimates ranging from 1.55 to 2.24, with confidence intervals ranging from 1.45 to 2.88, for an average generation time of 2.9 days, and shorter doubling times in some remote and isolated communities compared to urban centres, suggesting a more rapid spread of disease in these communities during the first wave. For the second wave, Re, the effective reproduction number, is estimated to be lower for remote and isolated communities; however, outbreaks appear to have been driven by somewhat higher transmissibility in urban centres. Conclusions There was considerable geographic variation in transmissibility of the 2009 pandemic outbreaks. While highlighting the importance of estimating R0 for informing health responses, the findings indicate that projecting the transmissibility for large-scale epidemics may not faithfully characterize the early spread of disease in remote and isolated communities.