<p>Mathematical modelling has become a powerful tool used to predict the spread of infectious diseases in populations. Successful analysis and modeling of historical infectious disease data can explain changes in the pattern of past epidemics and lead to a better understanding of epidemiological processes. The lessons learned can be used to predict future epidemics and help to improve public healthstrategies for control and eradication.</p> <p>This thesis is focused on the analysis and modelling of smallpox dynamics based on the weekly smallpox mortality records in London, England, 1664-1930. Statistical analysis of these records is presented. A timeline of significant historical events related to changes in variolation and vaccination uptake levels and demographics was established. These events were correlated with transitions observed in smallpox dynamics. Seasonality of the smallpox time series was investigated and the contact rate between susceptible and infectious individuals was found to be seasonally forced. Seasonal variations in smallpox transmission and changes in their seasonality over long time scale were estimated. The method of transition analysis, which is used to predict qualitative changes in epidemiological patterns, was used to explain the transitions observed in the smallpox time series. We found that the standard SIR model exhibits dynamics similar to the more realistic Gamma distributed SEIR model if the mean serial interval is chosen the same, so we used the standard SIR model for our analysis. We conclude that transitions observed in the temporal pattern of smallpox dynamics can be explained by the changes in birth, immigration and intervention uptake levels.</p> / Doctor of Philosophy (PhD)

Although there has been a recent surge in research examining comorbidity between externalizing and internalizing disorders in childhood, relatively less work has examined relations between specific externalizing conditions (i.e., oppositional defiant disorder (ODD) symptoms) and their co-occurrence with specific internalizing conditions (i.e., depressive symptoms). Furthermore, little empirical work has evaluated potential underlying processes, such as emotion dysregulation, which may explain relations between co-occurring ODD and depressive symptoms. There is also a paucity of research examining developmental patterns of co-occurring ODD and depressive symptoms. In the present study, I used latent class and latent transition analyses to (a) identify groups of children based on ODD and depressive symptom levels, (b) determine whether emotion dysregulation predicted co-occurring ODD and depressive symptoms, and (c) examine developmental patterns of change and continuity in groups across middle childhood within a community-based sample. Children were characterized by three latent classes based on ODD and depressive symptom severity: a group with very low levels of ODD or depressive symptoms, an ODD-only group with low levels of symptoms, and a co-occurring ODD and depressive symptom group with moderate levels of ODD and low levels of depressive symptoms. Furthermore, emotion dysregulation predicted to the class with moderate levels of ODD and low levels of depressive symptoms, although prediction from emotion dysregulation to class membership depended on the methodology used to index emotion dysregulation. Results of the LTA analyses suggested that symptom severity was relatively stable across middle childhood, with little evidence of changes in developmental patterns of ODD and depressive symptoms. Overall, the results of this study provide an important foundation for more sophisticated empirical inquiry regarding co-occurring ODD and depressive symptoms in childhood and potential processes that may explain their onset and development. / Psychology

A multitude of research demonstrates that ADHD is associated with negative psychological correlates and outcomes among children, such as academic difficulties and peer relationship problems. Youth with ADHD also experience high rates of comorbidity or co-occurring conditions, including mood, anxiety, oppositional defiant, and conduct disorders. However, few studies have investigated the development of co-occurring psychological symptoms among youth with ADHD over time and across different developmental periods. Shared risk factors likely contribute to the development of ODD, CD, anxiety, and depression among youth with ADHD. Emotion regulation and parenting style may confer risk or resilience for the development of co-occurring symptoms, but research is wanting. The current study examined an existing sample of youth who were recruited at age 10-12 and were followed at age 12-14 and 16. Analyses aimed to (a) identify subgroups of youth varying in type and levels of ADHD and co-occurring symptoms at three different time points using latent class analyses, (b) examine stability of membership and transitions to classes that differ in levels of ADHD and co-occurring symptoms using latent transition analyses, and (c) investigate emotion regulation and parenting style as predictors of stability and transitions among classes. Results revealed different patterns of ADHD and co-occurring symptoms, including a Low Symptoms class at each time point. Classes of youth with ADHD+Externalizing problems and ADHD+Internalizing problems emerged at ages 10-12 and 12-14. At age 16, two classes with qualitatively and quantitatively different externalizing and internalizing symptoms were identified. Latent transition analyses revealed transitions into the Low Symptoms class from each time point, but also stability and transitions to other symptomatic classes. Predictor analyses indicated that emotion regulation and parenting style were associated with transitions among and stability within classes, but findings were dependent on whether classes were defined primarily by co-occurring externalizing or internalizing symptoms. Results of the present study indicate that children with ADHD are likely to exhibit a range of psychological symptoms, but the frequency and quality of co-occurring symptoms may change over time. Emotion regulation and parenting may be potential targets for enhanced interventions among youth with ADHD with and without co-occurring symptoms. / Psychology

Objectives: Adherence to psychotropic medication is poor among individuals with bipolar disorder (BD). To understand treatment experiences and associated adherence among these individuals, we developed a novel construct of Clinical Net Benefit (CNB) using psychiatric symptoms, adverse effects and overall functioning assessments. We tested whether adherence differed across classes of CNB, whether individuals transitioned between classes over time, and whether these transitions were differentially associated with adherence. Methods: Data come from individuals aged 18+ during five years of the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD). Latent class analysis identified groups of CNB. Latent transition analysis determined probabilities of transitioning between classes over time. Adherence was defined as taking 75%+ of medications as prescribed. Associations between CNB and adherence were tested using multiple logistic regression adjusting for sociodemographic characteristics. Results: Five classes of CNB were identified during the first two years (high, moderately high, moderate, moderately low, low), and four classes (removing moderately high) during the last three years. Adherence did not differ across classes or time points. Medication regimens differed by class; those with higher CNB taking fewer medications had lower odds of adherence while those with lower CNB taking more medications had higher odds of adherence compared with monotherapy. Probability of transitioning from higher to lower CNB, and lower to higher CNB was greatest over time. Conclusions: CNB is heterogeneous in individuals treated for BD, and movement between classes is not uncommon. Understanding why individuals adhere despite suboptimal CNB may provide novel insights into aspects influencing adherence.

Abstract The contemporary information age is equipped with rich and affordable telecommunication services. In the future, people have even more flexibility when true wireless Internet and real-time multimedia are provided seamlessly over heterogeneous wireless networks. Optimally combining the capacity and services of the current and emerging networks requires a holistic view of mobility, resource and service management. This thesis contributes to the research and development of these hybrid systems with three main contributions. Firstly, a system architecture for vertical handoff in location-aware heterogeneous wireless networks is proposed. The proposed architecture enables the mobile node to prepare for approaching vertical handoffs and wake-up a hotspot interface. The needed communication procedures are discussed, and inter-related issues of mobility and geolocation information are considered in proportion to usability, advantages and limitations. Secondly, a framework for the analysis of vertical handoff algorithm sensitivity to various mobility parameters including velocity, handoff delay and dwell time is introduced. Handoff smoothing with a dwell-timer is analyzed as one potential scheme for optimizing vertical handoff locally. It is compared to a power based algorithm to find out its sensitivity to the changes in effective data rates, velocity of the terminal and the amount of handoff delay. The analysis focuses on the transition region, having case studies on both moving-in and moving-out scenarios. An optimal value for dwell-timer is found through simulations, showing a performance gain over power based algorithm as a function of mean throughput. The analysis is extended also to a multiple network scenario. Thirdly, experimental results on the behaviour of protocols used in wireless IP networks are presented. Prototype systems demonstrate results of using Mobile IP with a fuzzy logic algorithm for vertical handoff in a heterogeneous network environment and the role of IPv6 when using a voice application in a wireless LAN environment. Latest contributions include developing plug-and-play middleware functionalities for Symbian mobile devices, extending the use of the earlier results to state-of-the-art mobile devices.

Lo scopo generale della presente tesi è quello di arricchire la letteratura sul benessere finanziario dei giovani adulti adottando metodologie di ricerca e tecniche statistiche mai applicate in questo filone di ricerca. Nello specifico, nel primo capitolo è stata utilizzata la scoping methodology, ovvero una metodologia di sintesi della letteratura, con l’obiettivo di identificare la definizione, le componenti, i predittori e gli outcome del benessere finanziario dei giovani. Nel secondo capitolo è stata applicata la Latent Transition Analysis, con l’obiettivo di identificare sottogruppi omogenei di giovani rispetto ai marcatori dell’adultità che essi hanno già raggiunto e di verificare la relazione di tali sottogruppi con il benessere finanziario dei giovani che ad essi appartengono. Il terzo capitolo propone una metodologia per sviluppare e validare nuovi strumenti di misurazione, sulla base della visione contemporanea della validità. Tale metodologia, composta da tre diversi step, è stata utilizzata per la creazione di uno strumento adatto a misurare, su un campione di giovani italiani, il benessere finanziario soggettivo. Infine, il quarto capitolo riguarda la multiple informant methodology, che è stata utilizzata per raccogliere informazioni da madre, padre e figlio sul processo di socializzazione finanziaria familiare ed il suo impatto sul benessere finanziario del figlio. / The general aim of this research work is to enrich the literature on emerging adults’ financial well-being with research methodologies and statistical techniques never previously applied in this research field. Specifically, the first chapter of this thesis concerns the scoping methodology, a knowledge synthesis methodology that I adopted to identify the emerging adults’ financial well-being definition, components, predictors and outcomes. The second chapter consists in the application of a new statistical technique, Latent Transition Analysis, that I used to identify subgroups of emerging adults homogeneous in their configuration of adult social markers already reached and to investigate the relation between these emerging adults’ subgroups and their financial well-being. The third chapter describes a three-step methodology to develop and validate new measurement instruments, based on the contemporary view of validity proposed in the last fifty years. This three-step procedure was here applied to develop and validate a new instrument measuring subjective financial well-being for an emerging adult target population. Finally, the fourth chapter concerns the multiple informant methodology that I applied to collect information about family financial socialization and its impact on the child’s financial well-being from mother, father and the emerging adult child.

當資料屬於類別型的長期追蹤資料（Longitudinal categorical data）時，除了可以透過廣義估計方程式（General estimate equation, GEE）來求解模型參數估計值外，潛在移轉分析（Latent transition analysis, LTA）法也是一種可行的資料分析方法。若資料的期數不多，也可以選擇將資料適度分群後使用羅吉斯迴歸分析（Logistic regression）法。當探討的反應變數為二元（Binary）型態，且觀察對象於每一期提供多個測量變數值的情況之下，廣義估計方程式與羅吉斯迴歸分析法的使用，文獻上常見先將所有的測量變數值加總後，以「中位數」作為分類的切割點。不同於以上兩種方法，潛在移轉分析法則是直接使用原始資料來取得觀察對象的潛在狀態相關訊息，因此與前二者的作法不同，可能導致後續的各項分析結果有所差異存在。 為了能夠了解造成中位數分類法與移轉分析法差異的可能因素，我們架構在潛在移轉分析法的模型下，以不同的參數設定來進行電腦模擬，比較各參數條件下的兩分類方法差異。結果發現各潛在狀態下的測量變數反應機率形式、第一期潛在狀態的組成比例等皆會對兩分類方法是否具有相同分類有所影響。另外，透過分析「青少年媒體使用與健康生活調查」的實際資料得知，潛在移轉分析會將大部分的觀察對象歸屬於「網路成癮」，而中位數分類法則是將大部分的觀察對象歸屬於「無網路成癮」。此外，可以注意到「沮喪」、「線上情色每星期平均使用天數」、及「父母相處狀況」這幾個控制變數與各分組結果的關聯性，於上述三種資料分析方法中有所不同。 / Several methods can be used to analyze longitudinal categorical data, as among them Latent Transition Analysis (LTA), and Generalized Linear Models estimated by Generalized Estimating Equations (GEE) probably the most popular. In addition, if the number of periods is two, then with certain grouping of data, the Logistic Regression can also be applied to perform the analyses. When there are more than one manifest response variable for each study subject, LTA is able to classify the subjects in terms of the original manifest response variables and proceeds with necessary analyses. On the other hand, GEE method and Logistic Regression lack the flexibility, and require certain transformation to transform the manifest response variables into a categorical response variable first. One common way to form a binary response is to sum all manifest variables, and then taking median as a cut-point. In this study, we explore the differences of the classification resulted from LTA directly and using median as a cut-point through simulations. An empirical study is also provided to illustrate the classification differences, and the differences on the subsequent analyses using LTA, GEE method, and Logistic Regression approach.

Multi functional health monitoring wearable devices are quite prominent these days. Usually these devices are battery-operated and consequently are limited by their battery life (from few hours to a few weeks depending on the application). Of late, it was realized that these devices, which are currently being operated at fixed voltage and frequency, are capable of operating at multiple voltages and frequencies. By switching these voltages and frequencies to lower values based upon power requirements, these devices can achieve tremendous benefits in the form of energy savings. Dynamic Voltage and Frequency Scaling (DVFS) techniques have proven to be handy in this situation for an efficient trade-off between energy and timely behavior. Within imec, wearable devices make use of the indigenously developed MUSEIC v2 (Multi Sensor Integrated circuit version 2.0). This system is optimized for efficient and accurate collection, processing, and transfer of data from multiple (health) sensors. MUSEIC v2 has limited means in controlling the voltage and frequency dynamically. In this thesis we explore how traditional DVFS techniques can be applied to the MUSEIC v2. Experiments were conducted to find out the optimum power modes to efficiently operate and also to scale up-down the supply voltage and frequency. Considering the overhead caused when switching voltage and frequency, transition analysis was also done. Real-time and non real-time benchmarks were implemented based on these techniques and their performance results were obtained and analyzed. In this process, several state of the art scheduling algorithms and scaling techniques were reviewed in identifying a suitable technique. Using our proposed scaling technique implementation, we have achieved 86.95% power reduction in average, in contrast to the conventional way of the MUSEIC v2 chip’s processor operating at a fixed voltage and frequency. Techniques that include light sleep and deep sleep mode were also studied and implemented, which tested the system’s capability in accommodating Dynamic Power Management (DPM) techniques that can achieve greater benefits. A novel approach for implementing the deep sleep mechanism was also proposed and found that it can obtain up to 71.54% power savings, when compared to a traditional way of executing deep sleep mode. / Nuförtiden så har multifunktionella bärbara hälsoenheter fått en betydande roll. Dessa enheter drivs vanligtvis av batterier och är därför begränsade av batteritiden (från ett par timmar till ett par veckor beroende på tillämpningen). På senaste tiden har det framkommit att dessa enheter som används vid en fast spänning och frekvens kan användas vid flera spänningar och frekvenser. Genom att byta till lägre spänning och frekvens på grund av effektbehov så kan enheterna få enorma fördelar när det kommer till energibesparing. Dynamisk skalning av spänning och frekvens-tekniker (såkallad Dynamic Voltage and Frequency Scaling, DVFS) har visat sig vara användbara i detta sammanhang för en effektiv avvägning mellan energi och beteende. Hos Imec så använder sig bärbara enheter av den internt utvecklade MUSEIC v2 (Multi Sensor Integrated circuit version 2.0). Systemet är optimerat för effektiv och korrekt insamling, bearbetning och överföring av data från flera (hälso) sensorer. MUSEIC v2 har begränsad möjlighet att styra spänningen och frekvensen dynamiskt. I detta examensarbete undersöker vi hur traditionella DVFS-tekniker kan appliceras på MUSEIC v2. Experiment utfördes för att ta reda på de optimala effektlägena och för att effektivt kunna styra och även skala upp matningsspänningen och frekvensen. Eftersom att ”overhead” skapades vid växling av spänning och frekvens gjordes också en övergångsanalys. Realtidsoch icke-realtidskalkyler genomfördes baserat på dessa tekniker och resultaten sammanställdes och analyserades. I denna process granskades flera toppmoderna schemaläggningsalgoritmer och skalningstekniker för att hitta en lämplig teknik. Genom att använda vår föreslagna skalningsteknikimplementering har vi uppnått 86,95% effektreduktion i jämförelse med det konventionella sättet att MUSEIC v2-chipets processor arbetar med en fast spänning och frekvens. Tekniker som inkluderar lätt sömn och djupt sömnläge studerades och implementerades, vilket testade systemets förmåga att tillgodose DPM-tekniker (Dynamic Power Management) som kan uppnå ännu större fördelar. En ny metod för att genomföra den djupa sömnmekanismen föreslogs också och enligt erhållna resultat så kan den ge upp till 71,54% lägre energiförbrukning jämfört med det traditionella sättet att implementera djupt sömnläge.