Public health planning for influenza is based on morbidity and mortality estimates derived from statistical models. Lower than anticipated 2009 H1N1 pandemic death estimates have raised questions about the method. Examining the statistical method is important for future policy and program development. We compared the main methods of estimating influenza burden through a systematic literature review and by comparing statistical estimates of influenza-attributable burden at the Ottawa Hospital (TOH) to clinical estimates validated through chart review. We identified heterogeneity in methods used to estimate influenza-attributable mortality in the literature which resulted in within-season estimate variation by study. We found statistical estimates of influenza burden at TOH to be 4-8 times greater than clinically validated data. We also found no significant association between the outcomes examined and epidemic periods at TOH. The findings of this study suggest discordance between model estimates by model approach and between model estimates and validated findings. Examining reasons for these discordances should be pursued.

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Globally, the tourism industry is recognised as one of the fastest growing industries, generating high revenues and creating a vast number of job opportunities. In South Africa, this is no different and, in recent years, the tourism industry has outshone the country's gold exports therefore claiming its position as the fourth highest earner of foreign exchange to date. Yet the industry is still to receive the attention it deserves from conventional economics. This research aimed to fill this gap in South Africa by providing an understanding on the determinants of international tourism demand for South Africa. The first objective of the study was to provide a broad overview of the tourism industry of South Africa. The discussion focused on the supply and demand sides of tourism which, in turn, are divided into the domestic and international tourism markets. There has been a high growth, especially in the international market since 1994 and, while domestic and international markets continue to grow, seasonality remains an issue. Tourism has a significant impact on economic activity, employment, and the balance of payments and therefore the industry has great potential. The second objective was to create a theoretical understanding on the different factors that could determine the international demand for the tourism product. From this discussion it was found that there are various economic and non-economic factors that are believed to have an influence on tourism demand. Income, prices, transport cost, and the exchange rate are amongst the favourite economic variables with travel time, population, marketing expenditure, climate, and capacity being the more popular noneconomic factors. Among these, certain threats were also identified that could have harmful impacts on tourism growth. The third objective and main aim of the study was to determine which of the factors identified earlier determine the demand for international tourism to South Africa. This was done through an empirical investigation. Data from all the continents were used to attain an international perspective on tourist arrivals (tourism demand). The results indicated that capacity and climate factors determine tourism demand in the short term with income and transport cost influencing South Africa as a tourism destination in the long term. The last objective was to determine whether certain events or disasters that take place globally have a negative influence on tourism demand to South Africa. The event that was looked as was the terror attacks on the United States in September 2001. It was found that although the overall tourism activity of the world became stagnant during this period, the effect was not that considerable in South Africa's tourism arrivals. Tourism in countries such as the United Sates, on the other hand, has still not recovered fully after this event. / Thesis (M.Com. (International Commerce))--North-West University, Potchefstroom Campus, 2007.

Certain properties of optimal bitwise APP (A Posteriori Probability) decoding of binary linear block codes are studied. The focus is on the Probability Density Function (<i>pdf</i>) of the bit Log-Likelihood-Ratio (<i>LLR</i>). A general channel model with discrete (not necessarily binary) input and discrete or continuous output is considered. It is proved that under a set of mild conditions on the channel, the <i>pdf</i> of the bit <i>LLR</i> of a specific bit position is independent of the transmitted code-word. It is also shown that the <i>pdf</i> of a given bit <i>LLR</i>, when the corresponding bit takes the values of zero and one, are symmetric with respect to each other (reflection of one another with respect to the vertical axis). In the case of channels with binary inputs, a sufficient condition for two bit positions to have the same <i>pdf</i> is presented. An analytical method for approximate performance evaluation of binary linear block codes using an Additive White Gaussian Noise (AWGN) channel model with Binary Phase Shift Keying (BPSK) modulation is proposed. The pdf of the bit LLR is expressed in terms of the Gram-Charlier series expansion. This expansion requires knowledge of the statistical moments of the bit <i>LLR</i>. An analytical method for calculating these moments which is based on some recursive calculations involving certain weight enumerating functions of the code is introduced. It is proved that the approximation can be as accurate as desired, using enough numbers of terms in the Gram-Charlier series expansion. A new method for the performance evaluation of Turbo-Like Codes is presented. The method is based on estimating the <i>pdf</i> of the bit <i>LLR</i> by using an exponential model. The moment matching method is combined with the maximum entropy principle to estimate the parameters of the new model. A simple method is developed for computing the Probabilities of the Point Estimates (PPE) for the estimated parameters, as well as for the Bit Error Rate (BER). It is demonstrated that this method requires significantly fewer samples than the conventional Monte-Carlo (MC) simulation.

In this thesis, we investigate two expressions for symmetric group characters: Kerov?s universal character polynomials and Stanley?s character polynomials. We give a new explicit form for Kerov?s polynomials, which exactly evaluate the characters of the symmetric group scaled by degree and a constant. We use this explicit expression to obtain specific information about Kerov polynomials, including partial answers to positivity questions. We then use the expression obtained for Kerov?s polynomials to obtain results about Stanley?s character polynomials.

The problem of estimating latent or unobserved states of a dynamical system from observed data is studied in this thesis. Approximate filtering methods for discrete time series for a class of nonlinear systems are considered, which, in turn, require sampling from a partially specified discrete distribution. A new algorithm is proposed to sample from partially specified discrete distribution, where the specification is in terms of the first few moments of the distribution. This algorithm generates deterministic sigma points and corresponding probability weights, which match exactly a specified mean vector, a specified covariance matrix, the average of specified marginal skewness and the average of specified marginal kurtosis. Both the deterministic particles and the probability weights are given in closed form and no numerical optimization is required. This algorithm is then used in approximate Bayesian filtering for generation of particles and the associated probability weights which propagate higher order moment information about latent states. This method is extended to generate random sigma points (or particles) and corresponding probability weights that match the same moments. The algorithm is also shown to be useful in scenario generation for financial optimization. For a variety of important distributions, the proposed moment-matching algorithm for generating particles is shown to lead to approximation which is very close to maximum entropy approximation. In a separate, but related contribution to the field of nonlinear state estimation, a closed-form linear minimum variance filter is derived for the systems with stochastic parameter uncertainties. The expressions for eigenvalues of the perturbed filter are derived for comparison with eigenvalues of the unperturbed Kalman filter. Moment-matching approximation is proposed for the nonlinear systems with multiplicative stochastic noise.

A Thesis submitted to The University of Arizona College of Medicine - Phoenix in partial fulfillment of the requirements for the Degree of Doctor of Medicine. / Epilepsy is the most common neurologic condition seen in children. Focal cortical dysplasia (FCD), a seizure disorder characterized by abnormal cortical laminar development, comprises approximately 75% of medically intractable epilepsies in the pediatric population. A greater appreciation of the pathology and intrinsic properties of the epileptogenic zone may help in understanding why FCD lesions are drug‐resistant, and could potentially lead to more effective treatments in the pediatric population. Neuronal support cells such as microglia and astrocytes have shown to have a role in FCD pathology. These cells are also activated during aging and traumatic brain injury as evidence by morphological change. This study aims to characterize the spatial distribution of microglia and astrocytes using immunohistochemistry in dysplastic tissue of eight male pediatric patients diagnosed with FCD. Cortical specimens from patients who underwent surgical resection of focally dysplastic cortex at Phoenix Children’s Hospital between 2008 and 2014 were examined using immunohistochemistry. Primary antibodies against GFAP and Iba1, as well as structural staining using hematoxylin and eosin (H&E), were incubated on sections and further analyzed using bright‐field microscopy. A pattern of perivascular activated microglia was observed in five patients around at least one blood vessel, while a pattern of non‐localized ramified microglia was observed in the other three patients. No identifiable pattern of astrocytic distribution was found. Thus, distinct patterns of microglia, rather than astrocytes, suggest dual underlying mechanisms of epileptogenesis.

Clinical trials are typically conducted over a population within a defined time period in order to illuminate certain characteristics of a health issue or disease process. These cross-sectional studies provide a snapshot of these disease processes over a large number of people but do not allow us to model the temporal nature of disease, which is essential for modeling detailed prognostic predictions. Longitudinal studies, on the other hand, are used to explore how these processes develop over time in a number of people but can be expensive and time-consuming, and many studies only cover a relatively small window within the disease process. This thesis describes the application of intelligent data analysis techniques for extracting information from time series generated by different diseases. The aim of this thesis is to identify intermediate stages in a disease process and sub-categories of the disease exhibiting subtly different symptoms. It explores the use of a bootstrap technique that fits trajectories through the data generating “pseudo time-series”. It addresses issues including: how clinical variables interact as a disease progresses along the trajectories in the data; and how to automatically identify different disease states along these trajectories, as well as the transitions between them. The thesis documents how reliable time-series models can be created from large amounts of historical cross-sectional data and a novel relabling/latent variable approach has enabled the exploration of the temporal nature of disease progression. The proposed algorithms are tested extensively on simulated data and on three real clinical datasets. Finally, a study is carried out to explore whether we can “calibrate” pseudo time-series models with real longitudinal data in order to improve them. Plausible directions for future research are discussed at the end of the thesis.

Injuries have emerged as one of the biggest public health issues of the 21th century. Yet, the causal effects of injury control strategies are often questioned due to a lack of randomized experiments. In this thesis, a set of quasi-experimental methods are applied and discussed in the light of causal inference theory and the type of data commonly available in injury surveillance systems. I begin by defining the interrupted time series design as a special case of the regression-discontinuity design, and the method is applied to two empirical cases. The first is a ban on the sale and production of non-reduced ignition propensity (RIP) cigarettes, and the second is a tightening of the licensing rules for mopeds. A two-way fixed effects model is then applied to a case with time-varying starting dates, attempting to identify the causal effects of municipality-provided home help services for the elderly. Lastly, the effect of the Swedish bicycle helmet law is evaluated using the comparative interrupted time series and synthetic control methods. The results from the empirical studies suggest that the stricter licensing rules and the bicycle helmet law were effective in reducing injury rates, while the home help services and RIP cigarette interventions have had limited or no impact on safety as measured by fatalities and hospital admissions. I conclude that identification of the impact of injury control interventions is possible using low cost means. However, the ability to infer causality varies greatly by empirical case and method, which highlights the important role of causal inference theory in applied intervention research. While existing methods can be used with data from injury surveillance systems, additional improvements and development of new estimators specifically tailored for injury data will likely further enhance the ability to draw causal conclusions in natural settings. Implications for future research and recommendations for practice are also discussed. / Injuries have emerged as one of the biggest public health issues of the 21th century. Yet, the causal effects of injury control strategies are rarely known due to a lack of randomized experiments. In this thesis, a set of quasi-experimental methods are discussed in the light of causal inference theory and the type of data commonly available in injury surveillance systems. I begin by defining the identifying assumptions of the interrupted time series design as a special case of the regression-discontinuity design, and the method is applied to two empirical cases. The first is a ban on the sale and production of non-fire safe cigarettes and the second is a tightening of the licensing rules for mopeds. A fixed effects panel regression analysis is then applied to a case with time-varying starting dates, attempting to identify the causal effects of municipality-provided home help services for the elderly. Lastly, the causal effect of the Swedish bicycle helmet law is evaluated using a comparative interrupted time series design and a synthetic control design. I conclude that credible identification of the impact of injury control interventions is possible using simple and cost-effective means. Implications for future research and recommendations for practice are discussed.

For an equity-linked insurance, the death benefit is linked to the performance of the company’s investment portfolio. Hence, both mortality risk and equity return shall be considered for pricing such insurance. Several studies have found some dependence between mortality improvement and economy growth. In this thesis, we showed that American mortality rate and Dow Jones Industrial Average (DJIA) index price are negatively dependent by using several copulas to define the joint distribution. Then, we used these copulas to forecast mortality rates and index prices, and calculated the payoffs of a 10-year term equity-linked insurance. We showed that the predicted insurance payoffs will be smaller if dependence between mortality and index price is taken into account. / October 2016