Submitted by Biblioteca do Instituto de Física (bif@ndc.uff.br) on 2017-06-29T18:07:57Z No. of bitstreams: 1 dissertacao-marcelo-amanajas-pires-versao-final.pdf: 1960976 bytes, checksum: 9ad0a839c63b7d5fc5c96b2e62a10fe9 (MD5) / Made available in DSpace on 2017-06-29T18:07:57Z (GMT). No. of bitstreams: 1 dissertacao-marcelo-amanajas-pires-versao-final.pdf: 1960976 bytes, checksum: 9ad0a839c63b7d5fc5c96b2e62a10fe9 (MD5) / Empregando ferramentas da Física Estatística investigamos os possíveis cenários macroscópicos que emergem quando acopla-se uma dinâmica de epidemias sob campanha de vacinação com uma dinâmica de opiniões competitivas pró versus anti -vacina. Consideramos a abordagem de campo médio que é topologicamente equivalente a uma rede totalmente conectada. As mudanças de opinião seguem o modelo da Regra da Maioria. Os agentes anti-vacina seguem o modelo suscetível-infectado-suscetível(SIS) com taxas de transmissão λ e recuperação α, enquanto que os agentes a favor da vacinação vão vacinar-se com uma taxa γ, grau de engajamento, caso contrário seguem um modelo SIS com taxas (1 − γ)λ e α. Consideramos que a imunidade conferida pela vacina pode ser perdida com uma taxa de ressuscetibilidade φ. Os resultados analíticos em campo médio e simulações de Monte Carlo revelam um rico diagrama de cenários epidêmicos no curto prazo incluindo uma região onde os agentes pró-vacina mesmo em minoria inicial podem suprimir o surto epidêmico e outra região onde mesmo que toda a população inicial seja pró-vacina ainda há ocorrência de surto epidêmico se o grau de engajamento não for suficientemente alto. No longo prazo também observou-se uma diversidade de cenários interessantes: (i) tanto para φ = 0 quanto φ = 0 6 a pressão social tem um efeito duplo pois ela facilita a presença da fase endêmica quando a maioria inicial é anti-vacina, porém ela dificulta a persistência coletiva do contágio se a maioria inicial é pró-vacina; (ii) a transição de fase ativa-absorvente exibida pelo modelo epidêmico pode ser destruida se o grau de engajamento γ dos agentes pró-vacina é suficientemente alto e a vacina fornece imunidade temporária (φ 6= 0) (iii) para φ = 0 a densidade estacionária de infectados I∞ depende da densidade inicial de agentes pró-vacina de modo não-trivial. / By employing tools from Statistical Physics we investigated the macroscopic scenarios that can emerge from an epidemic spreading with vaccination under the impact of opinion dynamics with agents pro or anti-vaccine. We consider the mean-field approach which is topologically equivalent to a fully-connected network. The opinion changes are ruled by the majority-rule dynamics. Individuals against the vaccination follow a standard susceptible-infected-susceptible (SIS) model with spreading rate λ and recovery rate α, whereas the pro-vaccine individuals are vaccinated with rate γ otherwise they follow a SIS model with rates (1 − γ)λ and α. We consider that vaccine immunity can be lost with rate φ, the resusceptibility rate. Mean-Field calculations and Monte Carlo simulations reveal several interesting results. In the short-time limit we found evidences that: (i) even an initial minority in favor of the vaccination campaign can stop the disease spreading, if its engagement is sufficiently high; (ii) even if the entire population is pro-vaccine, an epidemic outbreaks can still occur if the engagement γ is not high enough. In the long term we also found many interesting macroscopic scenarios: (i) for φ = 0 and φ = 0 6 the social pressure acts as double edged sword since it hinders the disease prevalence when the initial majority is pro-vaccination, but it facilitates the disease persistence when the initial majority is against vaccination; (ii) the active-absorbing phase transition exhibited by the epidemic model can be suppressed if the engagement degree is high enough and the vaccination gives temporary immunity (φ = 0 6 ); (iii) for permanent immunity (φ = 0 ) the stationary density of Infected individuals has a non-trivial dependence on the initial density of pro-vaccine individuals.

Lorsqu'on pense à la santé, on se représente immédiatement le rôle des professions médicales, des hôpitaux et cliniques qui traitent la maladie. On ne pense généralement pas aux aménageurs. Mais que faire si l'on invite des urbanistes à contribuer aux efforts de la médecine préventive ? Comment adopter des stratégies d'aménagement qui conduisent à des modes de vie plus sains ?Après les actions conjuguées de la santé publique et de la planification urbaine dans leur lutte contre les épidémies et pour l'amélioration des conditions de vie dans les villes surpeuplées de la fin du 19 e siècle, les deux disciplines se sont séparées. Effectivement, bien que les deux métiers partagent des objectifs similaires, leurs approches méthodologiques diffèrent. Cependant, des décennies plus tard, les deux disciplines doivent se réunir à nouveau pour faire face aux nouvelles épidémies : les maladies chroniques (l'asthme, les allergies), auxquelles il faut ajouter les cancers, les maladies cardiovasculaires et pulmonaires, le diabète et l'obésité, qui semblent liées à la pollution (air, eau, sol) et à l'inactivité physique. Si le développement durable a mis l'accent sur la préservation de l'environnement, il a négligé les défis auxquels font face les populations urbaines défavorisées. L'inégalité territoriale s'aggrave en termes de santé publique. La tendance croissante de la certification et de la normalisation en matière d'aménagement durable peut être considérée comme une occasion de promouvoir la résilience en santé publique. La collaboration entre professionnels de la santé publique et aménageurs devrait favoriser le rapprochement de leurs stratégies / When we think about health, the first topic coming to mind is medical professionals, hospitals and clinics that treat the disease. We do not bind up urban planners together with public health concerns. But what if the planners are invited to contribute to preventive medicine? How to adopt urban plan strategies that lead to healthier lifestyles ? Since the combined actions of public health and urban planning fought against epidemics and improved living conditions in crowded cities of the late 19th century, the two disciplines have been both separated from each other. If medical profession and urban planners share similar tenets and strive towards the same goals, their methodological approaches are different. However, decades later, the two disciplines have to be reunited to address new epidemics such as chronic diseases (asthma, allergies), as well as cancers, cardiovascular and pulmonary diseases, diabetes and obesity, which seem related to pollution (air, water, soil) and physical inactivity. If sustainable development has focused on preserving the environment, it has neglected the challenges facing underprivileged population. The territorial inequality worsens in terms of public health. The growing trend of certification and standardization in sustainable development can be seen as an opportunity to promote public health resilience. Should collaborations between public health professionals and planners encourage the approximation of their strategies

Doctor Educationis / Background: In an attempt to avert the HIV/AIDS epidemic, more research has been conducted to determine why the epidemic is more devastating in Southern African countries than anywhere else in the world. Heterosexual transmission is believed to be driving the epidemic in many sub-Saharan African countries. Recent research has indicated that having concurrent sexual partners is one of the factors contributing to the fast spread of HIV transmission in this region.Aim: This study aimed to describe the level of knowledge about the risk of HIV transmission posed by concurrent sexual partnering as well as beliefs and practices about concurrent partnering among education students at the Rundu College of Education(RCE) in the Kavango region of Namibia. Concurrent partnering was defined as a situation where a person has more than one sexual partner at the same time, during the twelve months preceding the study.Methodology: There were 374 students registered for the 2009 academic year at RCE.All registered students were targeted for the study and 278 completed the questionnaire,yielding a response rate of 73.4%. The survey described prevalence of concurrent partnering, knowledge about risk posed by concurrent partnering as well as beliefs about concurrent partnering.Data collected was analyzed using Statistical Programs for Social Sciences (SPSS).Descriptive statistics were used to describe the prevalence of sexual concurrency,knowledge about risk posed by concurrent partnering and beliefs about concurrent partnering among the study population. Frequency of concurrency was cross tabulated with demographic variables like age group, sex and year of study as well as by knowledge and beliefs about sexual concurrent partnering.Results: The prevalence of concurrency in this sample was 9.4% with significantly higher prevalence (13.0%) among male students compared to females (5.3%). Males reported knowledge levels of 85.7% to 88.4% while females reported knowledge levels of 89.3% and 93.1%. More men (28.8%) than women (10.7%) agreed with the statement that sexual concurrency is a sign of manhood (p=0.00). Further, more male students(27.9%) compared to female students (6.1%) agreed with the statement that sexual concurrency is part of African culture and should continue (p=0.00).Conclusion: The study results show a high knowledge of risk posed by concurrency. It further reveals that a high number of people believe that concurrency is acceptable especially among men.HIV prevention activities promoting partner reduction and mutual fidelity should be implemented. Such activities should focus more on behavior change rather than on information giving. There is a need to create platforms for community members to debate on cultural beliefs about sexual concurrency.

I develop a novel method for computing globally accurate solutions to recur- sive macro-epidemic models featuring aggregate uncertainty and a potentially large number of state variables. Compared to the previous literature which either restricts attention to perfect-foresight economies amendable to sequence- space representation or focuses on highly simplified, low dimensional models that could can be analyzed using standard dynamic programming and linear projection techniques, I develop a deep learning-based algorithm that can han- dle rich environments featuring both aggregate uncertainty and large numbers of state variables. In addition to solving for particular model equilibria, I show how the deep learning method could be extended to solve for a whole set of models, indexed by the parameters of government policy. By pre-computing the whole equilibrium set, my deep learning method greatly simplifies compu- tation of optimal policies, since it bypasses the need to re-solve the model for many different values of policy parameters. 1

<div>Advances in information and communication technologies enable new paradigms for connectivity involving vehicles, infrastructure, and the broader road transportation system environment. Vehicle-to-vehicle (V2V) communications under the aegis of the connected vehicle are being leveraged for novel applications related to traffic safety, management, and control, which lead to a V2V-based traffic system. Within the framework of a V2V-based traffic system, this study proposes an integrated modeling framework to model the dynamics of a V2V-based traffic system that entails spatiotemporal interdependencies among the traffic flow dynamics, V2V communication constraints, the dynamics of information flow propagation, and V2V-based application. The proposed framework systematically exploits their spatiotemporal interdependencies by theoretical and computational approaches.</div><div>First, a graph-based multi-layer framework is proposed to model the V2V-based advanced traveler information system (ATIS) as a complex system which is comprised of coupled network layers. This framework addresses the dynamics of each physical vehicular traffic flow, inter-vehicle communication, and information flow propagation components within a layer, while capturing their interactions among layers. This enables the capabilities to transparently understand the spatiotemporal evolution of information flow propagation through a graph structure. A novel contribution is the systematic modeling of an evolving information flow network that is characterized as the manifestation of spatiotemporal events in the other two networks to enhance the understanding of the information flow evolution by capturing the dynamics of the interactions involving the traffic flow and the inter-vehicle communication layers. The graph-based approach enables the computationally efficient tracking of information propagation using a simple graph-based search algorithm and the computationally efficient storage of information through a single graph database.</div><div>Second, this dissertation proposes analytical approaches that enable theoretical investigation into the qualitative properties of information flow propagation speed. The proposed analytical models, motivated from spatiotemporal epidemiology, introduce the concept of an information flow propagation wave (IFPW) to facilitate the analysis of the information propagation characteristics and impacts of traffic dynamics at a macroscopic level. The first model consists of a system of difference equations in the discrete-space and discrete-time domains where an information dissemination is described in the upper layer and a vehicular traffic flow is modeled in the lower layer. This study further proposes a continuous-space and continuous-time analytical model that can provide a closed-form solution for the IFPW speed to establish an analytical relationship between the IFPW speed and the underlying traffic flow dynamics. It can corporate the effects of congested traffic, such as the backward traffic propagation wave, on information flow propagation. Thereby, it illustrates the linkage between information flow propagation and the underlying traffic dynamics. Further, it captures V2V communication constraints in a realistic manner using a probabilistic communication kernel (which captures the probability).<br></div><div>Third, within the integrated modeling framework, this dissertation captures the impact of information flow propagation on traffic safety and control applications. The proposed multi-anticipative forward collision warning system predicts the driver’s maneuver intention using a coupled hidden Markov model, which is one of statistical machine learning techniques. It significantly reduces the false alarm rates by addressing the uncertainty associate improves the performance of the future motion prediction, while currently available sensor-based kinematic models for addressing the uncertainty associated with the future motion prediction. A network-level simulation framework is developed to investigate a V2V-based ATIS in a large-scale network by capturing its inter-dependencies and feedback loop. This modeling framework provides the understanding of the relationship between the travelers’ routing decisions and information flow propagation.</div><div>This thesis provides a holistic understanding of information flow propagation characteristics in space and time by characterizing interactions among information flow propagation, and underlying traffic flow, and V2V communications characteristics. The proposed models and the closed-form solution of IFPW speed can help in designing effective V2V-based traffic systems, without relying on computationally expensive numerical methods. An innovative aspect of this approach represents a building block to develop both descriptive capabilities and prescriptive strategies related to propagating the flow of useful information efficiently and synergistically generating routing mechanisms that enhance the traffic network performance. Given the lack of appropriate methodologies to characterize the information flow propagation, this thesis expects to make a novel and significant contribution to understanding the characteristics of V2V-based traffic systems and their analysis.</div>

La dissémination collaborative d'une information d'un agent à tous les autres agents d'un système distribué est un problème fondamental qui est particulièrement important lorsque l'on veut obtenir des algorithmes distribués qui sont à la fois robustes et fonctionnent dans un cadre anonyme, c'est-à-dire sans supposer que les agents possèdent des identifiants distincts connus. Ce problème, connu sous le nom de problème de propagation de rumeur , est à la base de nombreux algorithmes de communication sur des réseaux de capteurs sans-fil [Dimakis et al. (2010)] ou des réseaux mobiles ad-hoc. Il est aussi une brique de base centrale pour de nombreux algorithmes distribués avancés [Mosk-Aoyama et Shah (2008)].Les méthodes les plus connues pour surmonter les défis de robustesse et d'anonymat sont les algorithmes basés sur les ragots ( gossip-based algorithms ), c'est-à-dire sur la paradigme que les agents contact aléatoirement les autres agents pour envoyer ou récupérer l'information. Nousproposons une méthode générale d'analyse de la performance des algorithmes basés sur les ragots dans les graphes complets. Contrairement aux résultats précédents basés sur la structure précise des processus étudiés, notre analyse est basée sur la probabilité et la covariance des évènements correspondants au fait qu'un agent non-informé s'informe. Cette universalité nous permet de reproduire les résultats basiques concernant les protocoles classiques de push, pull et push-pull ainsi qu'analyser les certaines variantions telles que les échecs de communications ou les communications simultanés multiples réalisées par chaque agent. De plus, nous sommescapables d'analyser les certains modèles dynamiques quand le réseaux forme un graphe aléatoire échantillonné à nouveau à chaque étape [Clementi et al. (ESA 2013)]. Malgré sa généralité, notre méthode est simple et précise. Elle nous permet de déterminer l'espérance du temps de la diffusion à une constante additive près, ce qu'il est plus précis que la plupart des résultatsprécédents. Nous montrons aussi que la déviation du temps de la diffusion par rapport à son espérance est inférieure d'une constante r avec la probabilité au moins 1 − exp(Ω(r)).À la fin, nous discutons d'une hypothèse classique que les agents peuvent répondre à plusieurs appels entrants. Nous observons que la restriction à un seul appel entrant par agent provoque une décélération importante du temps de la diffusion pour un protocole de push-pull. En particulier, une phase finale du processus prend le temps logarithmique au lieu du temps double logarithmique. De plus, cela augmente le nombre de messages passés de Θ(n log log n) (valeur optimale selon [Karp et al. (FOCS 2000)]) au Θ(n log n) . Nous proposons une variation simple du protocole de push-pull qui rétablit une phase double logarithmique à nouveau et donc le nombre de messages passés redescend sur sa valeur optimal. / Epidemic algorithms are distributed algorithms in which the agents in thenetwork involve peers similarly to the spread of epidemics. In this work, we focus on randomized rumor spreading -- a class of epidemic algorithms based on the paradigm that nodes call random neighbors and exchange information with these contacts. Randomized rumor spreading has found numerous applications from the consistency maintenance of replicated databases to newsspreading in social networks. Numerous mathematical analyses of different rumor spreading algorithms can be found in the literature. Some of them provide extremely sharp estimates for the performance of such processes, but most of them are based on the inherent properties of concrete algorithms.We develop new simple and generic method to analyze randomized rumor spreading processes in fully connected networks. In contrast to all previous works, which heavily exploit the precise definition of the process under investigation, we only need to understand the probability and the covariance of the events that uninformed nodes become informed. This universality allows us to easily analyze the classic push, pull, and push-pull protocols both in their pure version and in several variations such as when messages fail with constant probability or when nodes call a random number of others each round. Some dynamic models can be analyzed as well, e.g., when the network is a random graph sampled independently each round [Clementi et al. (ESA 2013)]. Despite this generality, our method determines the expected rumor spreading time precisely apart from additive constants, which is more precise than almost all previous works. We also prove tail bounds showing that a deviation from the expectation by more than an additive number of r rounds occurs with probability at most exp(−Ω(r)).We further use our method to discuss the common assumption that nodes can answer any number of incoming calls. We observe that the restriction that only one call can be answered leads to a significant increase of the runtime of the push-pull protocol. In particular, the double logarithmic end phase of the process now takes logarithmic time. This also increases the message complexity from the asymptotically optimal Θ(n log log n) [Karp, Shenker, Schindelhauer, Vöcking (FOCS 2000)] to Θ(n log n). We propose a simple variation of the push-pull protocol that reverts back to the double logarithmic end phase and thus to the Θ(n log log n) message complexity.

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[pt] Desde fevereiro de 2020 a pandemia gerada pelo novo coronavírus SarsCoV-2, vírus gerador da doença COVID-19, tem causado muitos óbitos, principalmente nos grandes centros urbanos. No Brasil, um dos estados mais afetados foi o Rio de Janeiro que, apesar de todas as ações feitas para mitigar o avanço da COVID-19, chegou em 01 de março de 2021 a uma taxa de mortalidade de 206,9 por cento, que corresponde a aproximadamente 207 óbitos a cada mil habitantes. No entanto, os municípios do RJ foram atingidos de maneira distinta, onde a cidade menos afetada alcançou 9,7 por cento e a mais afetada 331,3 por cento. Estudos prévios da literatura especializada indicam que a principal razão desta discrepância pode ser associada à fatores relacionados a população, renda, educação, saúde, economia, território e ambiente. Portanto, esse trabalho tem como principal objetivo identificar os principais fatores socioeconômicos, sociodemográficos e de acesso a recursos hospitalares que estão associadas a taxa de mortalidade oriunda do Sars-CoV-2 nos noventa e dois municípios do estado do Rio de Janeiro com base no modelo de Regressão de Poisson, no período de 01 de março de 2020 a 01 de março de 2021, contabilizando 12 meses. A partir do modelo escolhido foi possível detectar que dez dos onze fatores analisados influenciam na taxa de mortalidade. Sendo os fatores, Índice de desenvolvimento humano municipal (IDHM), Renda per capita (RDPC), Percentual de pobres (PMPOB), Produto interno bruto (PIB), Taxa de frequência bruta ao superior (T_FBSUPER), percentual de aglomerados subnormais (PER_AGSN), Densidade demográfica, Número de leitos hospitalares do SUS por habitante, Número de leitos hospitalares totais por habitante e Número de respiradores por habitante. Assim, os resultados obtidos com base nesses fatores analisados podem auxiliar na criação de ações mitigadoras mais direcionadas e eficientes, de acordo com as características dos municípios do estado do Rio de Janeiro. / [en] Since February 2020 the pandemic generated by the new coronavirus SarsCoV-2, the virus generating the disease COVID-19, has caused many deaths, mainly in large urban centers. In Brazil, one of the most affected states was Rio de Janeiro, which, despite all the actions taken to mitigate the progress of COVID19, reached on March 1, 2021 a mortality rate of 206.9 percent, which corresponds to approximately 207 deaths per thousand inhabitants. However, the Rio de Janeiro municipalities were affected differently, where the least affected city reached 9.7 percent and the most affected 331.3 percent. Previous studies in the specialized literature indicate that the main reason for this discrepancy may be associated with factors related to population, income, education, health, economy, territory, and environment. Therefore, this work has as main objective to identify the main socioeconomic, socio-demographic factors and access to hospital resources that are associated with the mortality rate from Sars-CoV-2 in the ninety-two municipalities in the state of Rio de Janeiro based on the Poisson Regression model, in the period from March 01, 2020 to March 01, 2021, accounting for 12 months. From the model chosen it was possible to detect those ten of the eleven factors analyzed influence the mortality rate. The factors being, municipal human development index (IDHM), per capita income (RDPC), percentage of poor (PMPOB), gross domestic product (GDP), gross attendance rate to higher (T_FBSUPER), percentage of subnormal settlements (PER_AGSN), demographic density, number of SUS hospital beds per inhabitant, number of total hospital beds per inhabitant and number of respirators per inhabitant. Thus, the results obtained based on these analyzed factors can help in the creation of more targeted and efficient mitigating actions, according to the characteristics of the municipalities in the state of Rio de Janeiro.

When attempting to mitigate the spread of an epidemic without the use of a vaccine, many measures may be made to dampen the spread of the disease such as physically distancing and wearing masks. The implementation of an effective test and quarantine strategy on a population has the potential to make a large impact on the spread of the disease as well. Testing and quarantining strategies become difficult when a portion of the population are asymptomatic spreaders of the disease. Additionally, a study has shown that randomly testing a portion of a population for asymptomatic individuals makes a small impact on the spread of a disease. This thesis simulates the transmission of the virus that causes COVID-19, SARSCoV- 2, in contact networks gathered from real world interactions in five different environments. In these simulations, several testing and quarantining strategies are implemented with a varying number of tests per day. These strategies include a random testing strategy and several uniform testing strategies, based on knowledge of the underlying network. By modeling the population interactions as a graph, we are able to extract properties of the graph and test based on those metrics, namely the degree of the network. This thesis found many of the strategies had a similar performance to randomly testing the population, save for testing by degree and testing the cliques of the graph, which was found to consistently outperform other strategies, especially on networks that are more dense. Additionally, we found that any testing and quarantining of a population could significantly reduce the peak number of infections in a community.

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