CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / Esta pesquisa busca interpretar as diversas experiÃncias vivenciadas pela populaÃÃo do Baixo Jaguaribe CearÃ, durante a epidemia de malÃria ao longo dos anos de 1937 a 1940. SerÃo analisadas as adversidades, mudanÃas e permanÃncias culturais que a peste palustre trazia para o dia-a-dia da regiÃo. Tais interferÃncias originaram uma crise na economia local, uma vez que o tempo do trabalho ficou submetido aos intervalos em que os acessos da doenÃa nÃo se manifestavam. Muitas safras, entÃo, ficaram perdidas e muito trabalho por ser realizado nas roÃas, nos carnaubais, nos pastos, nos algodoeiros, dentre outros, pois, em muitas residÃncias, a doenÃa se manifestou em todos os membros de uma mesma famÃlia. A incidÃncia da malÃria tambÃm ocasionou mudanÃas nos rituais fÃnebres: as pessoas nÃo acompanhavam os enterros, evitavam freqÃentar as sentinelas, os sinos das igrejas nÃo badalavam anunciando as mortes e, para alÃm destas, os padres da regiÃo nÃo conseguiam dar conta dos pedidos de extrema-unÃÃo aos moribundos. SerÃo ressaltadas tambÃm as diversas explicaÃÃes para o processo de erradicaÃÃo da doenÃa. Portanto, busca compreender a peste palustre para alÃm do seu carÃter patolÃgico, classificando-a como elemento responsÃvel por todo um processo de desorganizaÃÃo social. Dessa forma, ao optar por estudar a epidemia de malÃria, acabamos por tecer uma teia que envolve tanto os sentimentos, como as experiÃncias vivenciadas pelas pessoas atingidas pela mazela. / This research aims to understand the sort of experiences lived by the population of âBaixo Jaguaribeâ region, state of CearÃ, during the epidemics of malaria through 1937 until 1940. The adversities and the cultural permanency and changes the swampy plague brought to the routine of that region were analyzed. Such interferences originate a crisis in the local economy, once the time dedicated to laboring submitted itself to the intervals that the diseaseâs peak had not appeared. Sometimes the disease reached all the members of one single family, thereupon many crops were lost and also a lot of work to be done in the field (cultivated with carnaubas, cotton) and in the pasture were left behind. Due to the plague, some funeral rituais changed: people did not follow funerals and did not attend to the death-watch, the churchesâ bell did not toll to announce deaths in the community and, besides all this, the priests were not able to attend all the moribund requests to âextrema-unÃÃoâ. The whole set of explications to the process of eradication of the plague were highlighted in this research. Thus, swampy plague was understood beyond its pathologic aspect, referring to it as a component responsible for a complete social disorganization process. Therefore, as the option to study the epidemics of malaria was set, a complex network that embodies feelings as well as experiences lived by the people caught by the plague was found.

With online social networks such as Facebook and Twitter becoming globally popular, there is renewed interest in understanding the structural and dynamical properties of social networks. In this thesis we study several stochastic models arising in the context of the spread of influence or information in social networks. Our objective is to provide compact and accurate quantitative descriptions of the spread processes, to understand the effects of various system parameters, and to design policies for the control of such diffusions. One of the well established models for influence spread in social networks is the threshold model. An individual’s threshold indicates the minimum level of “influence” that must be exerted, by other members of the population engaged in some activity, before the individual will join the activity. We begin with the well-known Linear Threshold (LT) model introduced by Kempe et al. [1]. We analytically characterize the expected influence for a given initial set under the LT model, and provide an equivalent interpretation in terms of acyclic path probabilities in a Markov chain. We derive explicit optimal initial sets for some simple networks and also study the effectiveness of the Pagerank [2] algorithm for the problem of influence maximization. Using insights from our analytical characterization, we then propose a computationally efficient G1-sieving algorithm for influence maximization and show that it performs on par with the greedy algorithm, through experiments on a coauthorship dataset. The Markov chain characterisation gives only limited insights into the dynamics of influence spread and the effects of the various parameters. We next provide such insights in a restricted setting, namely that of a homogeneous version of the LT model but with a general threshold distribution, by taking the fluid limit of a probabilistically scaled version of the spread Markov process. We observe that the threshold distribution features in the fluid limit via its hazard function. We study the effect of various threshold distributions and show that the influence evolution can exhibit qualitatively different behaviors, depending on the threshold distribution, even in a homogeneous setting. We show that under the exponential threshold distribution, the LT model becomes equivalent to the SIR (Susceptible-Infected-Recovered) epidemic model [3]. We also show how our approach is easily amenable to networks with heterogeneous community structures. Hundreds of millions of people today interact with social networks via their mobile devices. If the peer-to-peer radios on such devices are used, then influence spread and information spread can take place opportunistically when pairs of such devices come in proximity. In this context, we develop a framework for content delivery in mobile opportunistic networks with joint evolution of content popularity and availability. We model the evolution of influence and content spread using a multi-layer controlled epidemic model, and, using the monotonicity properties of the o.d.e.s, prove that a time-threshold policy for copying to relay nodes is delay-cost optimal. Information spread occurs seldom in isolation on online social networks. Several contents might spread simultaneously, competing for the common resource of user attention. Hence, we turn our attention to the study of competition between content creators for a common population, across multiple social networks, as a non-cooperative game. We characterize the best response function, and observe that it has a threshold structure. We obtain the Nash equilibria and study the effect of cost parameters on the equilibrium budget allocation by the content creators. Another key aspect to capturing competition between contents, is to understand how a single end-user receives and processes content. Most social networks’ interface involves a timeline, a reverse chronological list of contents displayed to the user, similar to an email inbox. We study competition between content creators for visibility on a social network user’s timeline. We study a non-cooperative game among content creators over timelines of fixed size, show that the equilibrium rate of operation under a symmetric setting, exhibits a non-monotonic behavior with increasing number of players. We then consider timelines of infinite size, along with a behavioral model for user’s scanning behavior, while also accounting for variability in quality (influence weight) among content creators. We obtain integral equations, that capture the evolution of average influence of competing contents on a social network user’s timeline, and study various content competition formulations involving quality and quantity.

A backbone network is the central part of the communication network, which provides connectivity within the various systems across large distances. Disruptions in a backbone network would cause severe consequences which could manifest in the service outage on a large scale. Depending on the size and the importance of the network, its failure could leave a substantial impact on the area it is associated with. The failures of the network services could lead to a significant disturbance of human activities. Therefore, making backbone communication networks more resilient directly affects the resilience of the area. Contemporary urban and regional development overwhelmingly converges with the communication infrastructure expansion and their obvious mutual interconnections become more reciprocal. Spreading failures are of particular interest. They usually originate in a single network segment and then spread to the rest of network often causing a global collapse. Two types of spreading failures are given focus, namely: epidemics and cascading failures. How to make backbone networks more resilient against spreading failures? How to tune the topology or additionally protect nodes or links in order to mitigate an effect of the potential failure? Those are the main questions addressed in this thesis. First, the epidemic phenomena are discussed. The subjects of epidemic modeling and identification of the most influential spreaders are addressed using a proposed Linear Time-Invariant (LTI) system approach. Throughout the years, LTI system theory has been used mostly to describe electrical circuits and networks. LTI is suitable to characterize the behavior of the system consisting of numerous interconnected components. The results presented in this thesis show that the same mathematical toolbox could be used for the complex network analysis. Then, cascading failures are discussed. Like any system which can be modeled using an interdependence graph with limited capacity of either nodes or edges, backbone networks are prone to cascades. Numerical simulations are used to model such failures. The resilience of European National Research and Education Networks (NREN) is assessed, weak points and critical areas of the network are identified and the suggestions for its modification are proposed.

Rinderpest, the most dreaded bovine plague, struck the cattle of the BaSotho in British Basutoland early in 1897. By December the murrain had spent itself, having reduced the cattle population by half As it did so, the rinderpest claimed the primary historical significance of an epidemic. By sharpening behaviour and illuminating latent or developing tendencies, the rinderpest helped to reveal the nooks and crannies of contemporary historical processes that would have otherwise eluded historical visibility. This thesis brings out the complexities and ambiguities surrounding the epidemic. It uses the crisis occasioned by the panzootic in its multifaceted manifestations as a prism through which we might view the complex aspects of contemporary historical processes. It goes beyond the narrow limits of the crisis itself to discerning the broader and wider historical patterns that the rinderpest helped to highlight.

The fight against epidemics/pandemics is one of man versus nature. Technological advances have not only improved existing methods for monitoring and controlling disease outbreaks, but have also provided new means for investigation, such as through modeling and simulation. This dissertation explores the relationship between social structure and disease dynamics. Social structures are modeled as graphs, and outbreaks are simulated based on a well-recognized standard, the susceptible-infectious-removed (SIR) paradigm. Two independent, but related, studies are presented. The first involves measuring the severity of outbreaks as social network parameters are altered. The second study investigates the efficacy of various vaccination policies based on social structure. Three disease-related centrality measures are introduced, contact, transmission, and spread centrality, which are related to previously established centrality measures degree, betweenness, and closeness, respectively. The results of experiments presented in this dissertation indicate that reducing the neighborhood size along with outside-of-neighborhood contacts diminishes the severity of disease outbreaks. Vaccination strategies can effectively reduce these parameters. Additionally, vaccination policies that target individuals with high centrality are generally shown to be slightly more effective than a random vaccination policy. These results combined with past and future studies will assist public health officials in their effort to minimize the effects of inevitable disease epidemics/pandemics.

This dissertation proposes a theoretical framework of attitude change under threatening conditions based on parallel updating. More specifically, I focus on public preferences for policies to address terrorist attacks, pandemics, climate change and natural disasters in periods when these threats are elevated. I test my argument with four original survey experiments, which include eleven interventions and draw on a nationally diverse sample of a total of 9,110 American citizens. These interventions identify the effects of factual information, partisan cues, incidental emotions, ideological and non-ideological framing, and memory priming. Evidence from these experiments provides consistent support that public opinion updating exhibits five characteristics. First, citizens change their views by a small amount. Second, citizens’ opinions move in the direction of information. Third, attitude change occurs regardless of political predispositions and individual attributes. Fourth, exposure to information about a specific policy area does not impact preferences for policies unrelated to this area. The only exception to this rule is when the treatment is emotionally strong. Finally, attitude- and identity-based cross pressures may introduce only minimal bias in the manner citizens update their opinions. These conclusions strongly challenge theories of public opinion which argue that individual differences in more-or-less enduring political and psychological characteristics can lead to political polarization. Although the persuasive techniques studied here are not equally potent in changing political views, the findings invite cautious optimism about the capacity of citizens to update opinions in a reasonable and accurate manner, even when the circumstances are unfavorable. Finally, the results suggest that the roots of polarization should be searched for more directly, notably in the increasingly fragmented political, social, and media environments.

[pt] Este trabalho modela a evolução temporal de uma epidemia com uma abordagem estocástica. O número de novas infecções por infectado é modelado como uma variável aleatória discreta, chamada aqui de contágio. Logo, a evolução temporal da doença é um processo estocástico. Mais especificamente, a propagação é dada pelo modelo de Bienaymé-Galton-Watson, um tipo de processo de ramificação de parâmetro discreto. Neste processo, para um determinado instante, o número de membros infectados, ou seja, a geração de membros infectados é uma variável aleatória. Na primeira parte da dissertação, dado que o modelo probabilístico do contágio é conhecido, quatro metodologias utilizadas para obter as funções de massa das gerações do processo estocástico são comparadas. As metodologias são: funções geradoras de probabilidade com e sem identidades polinomiais, cadeia de Markov e simulações de Monte Carlo. A primeira e terceira metodologias fornecem expressões analíticas relacionando a variável aleatória de contágio com a variável aleatória do tamanho de uma geração. Essas expressões analíticas são utilizadas na segunda parte desta dissertação, na qual o problema clássico de inferência paramétrica bayesiana é estudado. Com a ajuda do teorema de Bayes, parâmetros da variável aleatória de contágio são inferidos a partir de realizações do processo de ramificação. As expressões analíticas obtidas na primeira parte do trabalho são usadas para construir funções de verossimilhança apropriadas. Para resolver o problema inverso, duas maneiras diferentes de se usar dados provindos do processo de Bienaymé-Galton-Watson são desenvolvidas e comparadas: quando dados são realizações de uma única geração do processo de ramificação ou quando os dados são uma única realização do processo de ramificação observada ao longo de uma quantidade de gerações. O critério abordado neste trabalho para encerrar o processo de atualização na inferência paramétrica usa a distância de L2-Wasserstein, que é uma métrica baseada no transporte ótimo de massa. Todas as rotinas numéricas e simbólicas desenvolvidas neste trabalho são escritas em MATLAB. / [en] This work models an epidemic s spreading over time with a stochastic approach. The number of infections per infector is modeled as a discrete random variable, named here as contagion. Therefore, the evolution of the disease over time is a stochastic process. More specifically, this propagation is modeled as the Bienaymé-Galton-Watson process, one kind of branching process with discrete parameter. In this process, for a given time, the number of infected members, i.e. a generation of infected members, is a random variable. In the first part of this dissertation, given that the mass function of the contagion s random variable is known, four methodologies to find the mass function of the generations of the stochastic process are compared. The methodologies are: probability generating functions with and without polynomial identities, Markov chain and Monte Carlo simulations. The first and the third methodologies provide analytical expressions relating the contagion random variable and the generation s size random variable. These analytical expressions are used in the second part of this dissertation, where a classical inverse problem of bayesian parametric inference is studied. With the help of Bayes rule, parameters of the contagion random variable are inferred from realizations of the stochastic process. The analytical expressions obtained in the first part of the work are used to build appropriate likelihood functions. In order to solve the inverse problem, two different ways of using data from the Bienaymé-Galton-Watson process are developed and compared: when data are realizations of a single generation of the branching process and when data is just one realization of the branching process observed over a certain number of generations. The criteria used in this work to stop the update process in the bayesian parametric inference uses the L2-Wasserstein distance, which is a metric based on optimal mass transference. All numerical and symbolical routines developed to this work are written in MATLAB.

Die große Menge an Datensätzen, die in den letzten Jahren verfügbar wurden, hat es ermöglicht, sowohl menschlich-getriebene als auch biologische komplexe Systeme in einem beispiellosen Ausmaß empirisch zu untersuchen. Parallel dazu ist die Vorhersage und Kontrolle epidemischer Ausbrüche für Fragen der öffentlichen Gesundheit sehr wichtig geworden. In dieser Arbeit untersuchen wir einige wichtige Aspekte von Diffusionsphänomenen und Ausbreitungsprozeßen auf Netzwerken. Wir untersuchen drei verschiedene Probleme im Zusammenhang mit Ausbreitungsprozeßen im überkritischen Regime. Zunächst untersuchen wir die Reaktionsdiffusion auf Ensembles zufälliger Netzwerke, die durch die beobachteten Levy-Flugeigenschaften der menschlichen Mobilität charakterisiert sind. Das zweite Problem ist die Schätzung der Ankunftszeiten globaler Pandemien. Zu diesem Zweck leiten wir geeignete verborgene Geometrien netzgetriebener Streuprozeße, unter Nutzung der Random-Walk-Theorie, her und identifizieren diese. Durch die Definition von effective distances wird das Problem komplexer raumzeitlicher Muster auf einfache, homogene Wellenausbreitungsmuster reduziert. Drittens führen wir durch die Einbettung von Knoten in den verborgenen Raum, der durch effective distances im Netzwerk definiert ist, eine neuartige Netzwerkzentralität ein, die ViralRank genannt wird und quantifiziert, wie nahe ein Knoten, im Durchschnitt, den anderen Knoten im Netzwerk ist. Diese drei Studien bilden einen einheitlichen Rahmen zur Charakterisierung von Diffusions- und Ausbreitungsprozeßen, die sich auf komplexen Netzwerken allgemein abzeichnen, und bieten neue Ansätze für herausfordernde theoretische Probleme, die für die Bewertung künftiger Modelle verwendet werden können. / The large amount of datasets that became available in recent years has made it possible to empirically study humanly-driven, as well as biological complex systems to an unprecedented extent. In parallel, the prediction and control of epidemic outbreaks have become very important for public health issues. In this thesis, we investigate some important aspects of diffusion phenomena and spreading processes unfolding on networks. We study three different problems related to spreading processes in the supercritical regime. First, we study reaction-diffusion on ensembles of random networks characterized by the observed Levy-flight properties of human mobility. The second problem is the estimation of the arrival times of global pandemics. To this end, we derive and identify suitable hidden geometries of network-driven spreading processes, leveraging on random-walk theory. Through the definition of network effective distances, the problem of complex spatiotemporal patterns is reduced to simple, homogeneous wave propagation patterns. Third, by embedding nodes in the hidden space defined by network effective distances, we introduce a novel network centrality, called ViralRank, which quantifies how close a node is, on average, to the other nodes. These three studies constitute a unified framework to characterize diffusion and spreading processes unfolding on complex networks in very general settings, and provide new approaches to challenging theoretical problems that can be used to benchmark future models.

The pandemic produced by the COVID-19 virus has resulted in an estimated 6.4 million deaths worldwide and a rise in unemployment rates, notably in the UK. Healthcare monitoring systems encounter several obstacles when regulating and anticipating epidemics. The study aims to present the AF-HIDOP model, an artificial neural network Fast Fourier Transform hybrid technique, for the early identification and prediction of the risk of Covid-19 spreading within a specific time and region. The model consists of the following five stages: 1) Data collection and preprocessing from reliable sources; 2) Optimal machine learning algorithm selection, with the Random Forest tree (RF) classifier achieving 94.4% accuracy; 3) Dimensionality reduction utilising principal components analysis (PCA) to optimise the impact of the data volume; 4) Predicting case numbers utilising an artificial neural network model, with 52% accuracy; 5) Enhancing accuracy by incorporating Fast Fourier Transform (FFT) feature extraction and ANN, resulting in 91% accuracy for multi-level spread risk classification. The AF-HIDOP model provides prediction accuracy ranging from moderate to high, addressing issues in healthcare-based datasets and costs of computing, and may have potential uses in monitoring and managing infectious disease epidemics.

Thesis (MSc (Medical Virology )--University of Stellenbosch, 2004. / 150 leaves printed on single pages, preliminary pages i-vii and numberd pages 1-143. Includes bibliography and figures digitized at 300 dpi grayscale and 300 dpi 24-bit Color to pdf format (OCR), using a Hp Scanjet 8250 Scanner and digitized at 600 dpi grayscale to pdf format (OCR), using a Bizhub 250 Konica Minolta Scanner. / ENGLISH ABSTRACT: The first episode of HIV-1 in South Africa was documented in 1982. Homosexual transmission of the virus was the predominate mode of transmission in an epidemic of mainly HIV-1 subtype Band D infections. To date, no full-length sequences of Subtype D strains from South Africa has been reported. Here we describe the characterization and some of the unique features of the Tygerberg HIV-1 subtype D strains. A near full-length 9 kb fragment was obtained through a one step PCR using high molecular weight DNA. Cloning was done successfully with the pCR-XLTapa cloning kit. Large quantities of plasmid DNA was grown and sequenced on both strands of the DNA. ORF determination and subtyping was followed by standard phylogenetic methods to construct evolutionary phylogenetic trees. Subtyping and similarity plots revealed that the sequences from Tygerberg are pure subtype D. All the Tygerberg strains had intact genes with no premature stop codons. At the tip of the V3 loop, the Tygerberg strains have the GOGO motif. R214 has a more variable vpu gene than the rest of the Tygerberg strains, but is still subtype D in this region. No premature stop codons have been observed in the tat gene and the glycosilation of the strains are less than the subtype D consensus. We are the first to report full-length sequences of HIV-1 subtype D strains from South Africa. The sequences represent non-mosaic genomes of subtype D. Our results confirm that the subtype D sequences from the beginning of the HIV-1 epidemic differ from the Subtype D sequences from recent isolates. / AFRIKAANSE OPSOMMING: Die eerste episode van HIV-1 infeksie in Suid Afrika is in 1982 gedokumenteer. Die epidemie het hoofsaaklik uit subtipe B en D bestaan en was deur homoseksuele kontak oorgedra. Geen vollengte subtipe D DNS volgordes van Suid-Afrika is tans beskryf nie. Hier beskryf ons die karakterisering van vollengte subtipe D stamme asook sommige van die unieke eienskappe van die virusse. Die vollengte 9 kb genoom volgorde was verkry deur 'n eenstap PKR reaksie met hoë molekulêre gewig DNS uit te voer. Die 9 kb fragment was suksesvol gekloneer met behulp van die peR-Xl-TOPO klonerings toetsstel. Groot hoeveelhede plasmied DNS was opgegroei en die nukleotied volgorde bepaal op beide stringe van die genoom. Die stamme was gesubtipeer en filogenetiese analise was uitgevoer met standaard metodes. Die volledige DNS volgordes was bepaal en subtipering het daarop gedui dat die stamme van Tygerberg suiwer subtipe D is. Geen premature stop kodons is in die nukleotied volgordes van die Tygerberg stamme gevind nie. By die draai van die varieerbare deel (V3) het al die Tygerberg stamme die GQGQ motief gehad. R214 het 'n meer varieerbare vpu geen, maar behoort steeds tot die subtipe D groep in die gedeelte. Daar was geen premature stop kodons in die tat geen gevind nie en die glikosilasie van die stamme is minder as die van die konsensus subtipe D stam. Ons is die eerste groep om vollengte subtipe D stamme van Suid-Afrika te karakteriseer. Die DNS volgordes verteenwoordig suiwer subtipe D genome. Ons resultate bevestig die van ander dat die nukleotied volgordes van die ouer subtipe D stamme verskil van die nuwer stamme.