<div>Public health policies increasingly rely on complex models that need to approximate epidemics realistically and be consistent with the available data. Choosing appropriate simplifying assumptions is one of the critical challenges in disease modeling. In this thesis, we focus on some of these assumptions to show how they impact model outcomes. </div><div>In this thesis, an ODE model with a gamma-distributed infectious period is studied and compared with an exponentially distributed infectious period. We show that, for childhood diseases, isolating infected children is a possible mechanism causing oscillatory behavior in incidence. This is shown analytically by identifying a Hopf bifurcation with the isolation period as the bifurcation parameter. The threshold value for isolation to generate sustained oscillations from the model with gamma-distributed isolation period is much more realistic than the exponentially distributed model.</div><div><br></div><div>The consequences of not modeling the spectrum of clinical symptoms of the 2014 Ebola outbreak in Liberia include overestimating the basic reproduction number and effectiveness of control measures. The outcome of this model is compared with those of models with typical symptoms, excluding moderate ones. Our model captures the dynamics of the recent outbreak of Ebola in Liberia better, and the basic reproduction number is more consistent with the WHO response team's estimate. Additionally, the model with only typical symptoms overestimates the basic reproduction number and effectiveness of control measures and exaggerates changes in peak size attributable to interventions' timing.</div><div><br></div>

Philosophiae Doctor - PhD / The current study was, in part, prompted by the high case fatality rates for severe acute malnutrition in two district hospitals in the Eastern Cape Province in South Africa. These case fatality rates were being attributed to Human Immunodeficiency Virus infection rather than to mismanagement by nurses involved in the hospital management of SAM cases. There were also some anecdotes from clinicians in the same hospitals that, depending on the clinical stage of HIV infection, the World Health Organisation's ten-step protocol may show no effect. This left some uncertainties as to whether these guidelines are suitably designed for use during the management of HIV positive children who are severely malnourished and at different HIV clinical stages. This study sought to reinforce the design of a longstanding facility-based intervention originally developed to improve the management of severe acute malnutrition in two district hospitals in South Africa. The aim was to design an improved intervention which was implemented and evaluated to determine its potential effect on treatment outcomes, specifically in the context of high HIV comorbidity. The study also sought to provide the context for the effectiveness of this intervention, in terms of its implementation fidelity and associated moderating factors. Lastly, the study evaluated the sustainability of the intervention after it was discontinued. Methods The current study reports on the development, implementation and evaluation of an intervention to improve the management of severe acute malnutrition in two district hospitals in the Eastern Cape Province. A Sequential Explanatory Mixed Method Design was used. During the study, the effect of HIV infection, disease stage and other clinical characteristics on the survival of children with severe acute malnutrition was assessed. The relationship between the rate of weight gain and duration of hospitalisation based on HIV status and disease stage were also examined. The data were collected prospectively during the study using retrospective record review of a total of 450 severely malnourished children who were admitted and treated at the two facilities from 2009 to 2013.A pre-tested 76- item patient evaluation form was used to collect data on patient characteristics on admission, treatment processes and outcomes. Data analysis was performed using STATA13.0 and involved simple descriptive computation of quantitative variables as well as non-parametric tests to compare groups between and within hospitals. Kaplan-Meier curves and Cox proportional hazard modelling were used to analyse time to event data. The study also assessed the impact of the intervention at time intervals on outcomes of interest. The analysis focused on modelling and plotting monthly mortality statistics collected over a period of 69 months. This was done to detect related trend and level changes before, immediately (after the first two months) and after (following the two months) the removal of the intervention. Lastly ethnographic and focus group enquiries were used to explain the quantitative results. Two focus group discussions were held in each hospital with clinicians and the management staff. This was done at the end of phase three. The focus group data were analysed using the framework analysis approach.

Context : Alzheimer&apos;s disease is a memory impairment disease which mostly affects elderly people. Currently, about 4 million Americans and 5 million Europeans are affected by this disease. The occurrence of Alzheimer&apos;s disease is expected to quadruple by the year 2020. Alzheimer&apos;s disease cannot be cured or stopped its progression rather delay its progression. Early diagnosis of the disease helps the patients, the caregivers and health institutions to save time, cost and minimize patients suffering. Objectives : In this thesis, different machine learning algorithms used for classification purpose are evaluated and various Alzheimer&apos;s disease diagnosis techniques are identified. Among these algorithms, a suitable classifier that has better classification accuracy on the National Alzheimer Coordinating Center (NACC) dataset is selected. This classifier is customized in order to make it compatible for the NACC dataset and to receive the new instance from the user. Then a multi-agent system model is develop that can improve the classification accuracy. Methods : Different research works are reviewed and experiments are conducted throughout this research work. A dataset for this research is obtained from National Alzheimer&apos;s Coordinating Center, university of Washington. Using this dataset, two experiments are conducted in WEKA. In the first experiment, the five candidate algorithms are compared to select the significant classifier for medical history and cognitive function data. For the second experiment, two datasets are used; a dataset contains Medical History (MH) with Cognitive Function (CF) data and a dataset that contains only medical history data to check in which dataset the selected classifier has better accuracy. Results : From the first experiment, J48 classifier has a better stage prediction accuracy than the candidate algorithms with 61.12%. J48 is customized to classify a new instance received from the user and to improve the classification accuracy. Then the accuracy increase to 87.09% when the classifier&apos;s parameters are optimized. When the medical history and cognitive function data is experimented in WEKA separately, the classification accuracies of J48 on MH, CF and their combination datasets are 81.42%, 64.20% and 87.09% respectively. The agents simulation result showed that some misclassified instances by J48 algorithm can be corrected by using multi agent system. The experimental results are presented in graphical format. Conclusions : Hence we conclude that machine learning and agent system in combination can be used for Alzheimer&apos;s disease diagnosis and its stage prediction by extracting knowledge from a dataset which contains patients medical history and cognitive function data. / Syftet med detta examensarbeta var att diagnostiserar Alzheimer patienter använder mönstret från en samling av andra tidigare diagnoserad patienter information och diagnosdata. Examensarbete hade tre huvuduppgifter: Förberedelse av data (mer än 10000 patienter data) för forskningen, maskininlärning algoritmer utvärderade med WEKA verktyg för att välja den bästa algoritmen och förbättra noggrannheten av den valda algoritmen med hjälp av agent system tekniker . - SQL queries används på uppgifter förberedelsefas. - WEKA programvara används för algoritmer utvärdering. - Agent arkitektur är utvecklat för att förbättra förutsäga av noggrannhets. Det bidrag av detta examensarbeta är identifiera Alzheimer patienter diagnos metod som använder en samling av patienternas diagnos information / biliyala.ezd2@gmail.com, them22dayz@gmail.com

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