A dinâmica de movimentação de animais em uma rede de propriedades rurais e o espalhamento de algumas doenças animais estão intrinsecamente relacionados. Assim, compreender a dinâmica do espalhamento de doenças infecciosas nestas redes é um instrumento importante no controle destas. Neste projeto, foram implementados algoritmos para gerar redes de movimentação de animais hipotéticas e reconstruiu-se a rede de movimentações de bovinos do Estado do Mato Grosso, 2007, Brasil. Foram feitas diversas simulações a fim de verificar o espalhamento de doenças agudas e crônicas nessas redes. Diferentes dinâmicas de espalhamento de doenças infecciosas foram observadas em redes com a mesma distribuição de graus e diferentes estruturas topológicas. Espera-se que os resultados das simulações matemáticas possam auxiliar nas atividades dos órgãos responsáveis pela vigilância epidemiológica e incentivar outros Estados a seguirem o exemplo do Estado do Mato Grosso, a construírem bancos de dados que possam ser analisados utilizando a metodologia de redes. / The animals\' movements in a farms network and the spread of some animal diseases are intrinsically related. Therefore, comprehending the dynamics of the spreading of infectious diseases in these networks is an important tool in controlling these diseases. In this project, we have implemented algorithms to generate hypothetical networks of animals\' movements and rebuilt the network of bovine movements from the State of Mato Grosso, 2007, Brazil. We made several simulations in order to check the spreading of acute and chronic disease in these networks. Different dynamics of infectious disease spreading were observed in networks with the same degree distribution and different topological structure. We hope that the results of the mathematical simulations may assist in the activities of agencies responsible for disease surveillance and encourage other States to follow the example of the State of Mato Grosso, to build databases that can be analyzed using the methodology of networks.

A movimentação de animais em uma rede de fazendas e o espalhamento de algumas doenças animais estão intrinsecamente relacionados. Assim, compreender a dinâmica do espalhamento de doenças infecciosas nestas redes é um instrumento importante no controle dessas doenças. Usando as informações sobre as movimentações de bovinos no estado de Mato Grosso, Brasil, em 2007, reconstruiu-se a rede de trânsito e a rede de proximidade geográfica entre os estabelecimentos desse estado, além de redes hipotéticas seguindo os modelos de rede Molloy-Reed, Kalisky, Método A e Método B, onde simulou-se, usando diferentes configurações do modelo SLIRS, o espalhamento de doenças com parâmetros hipotéticos e reais (brucelose e febre aftosa). Além disso, simulou-se o controle do espalhamento dessas doenças considerando o controle por imunização e por restrição, com e sem rearranjo das movimentações após a restrição, selecionando os estabelecimentos a serem protegidos de forma aleatória, baseando-se no grau de movimentação dos animais e utilizando o conceito do paradoxo da amizade. Dentre os resultados, destacam-se que apesar dos padrões das curvas de prevalência nas redes hipotéticas serem semelhantes aos da rede real, os valores observados foram maiores nas redes hipotéticas, indicando que utilizá-las no planejamento de políticas de controle de doenças no lugar da rede real pode levar a um maior uso de recursos do que seria necessário. Além disso, no controle das doenças tanto com parâmetros hipotéticos quanto com parâmetros reais, nas simulações usando apenas a rede de trânsito dos animais, observou-se uma redução mais efetiva da prevalência ao se selecionar os estabelecimentos com maior grau total do que a da seleção aleatória, enquanto que nas simulações que consideraram a rede de proximidade geográfica dos estabelecimentos, a redução na prevalência das estratégias que selecionaram estabelecimentos específicos foram semelhantes aos da seleção aleatória. Sobre o efeito do rearranjo das movimentações, observou-se que este pode facilitar o espalhamento de doenças na rede, mesmo nas situações em que se aplica alguma estratégia de controle. Espera-se que os resultados das simulações matemáticas possam contribuir para a discussão do impacto relativo entre as estratégias de controle mencionadas e que futuramente possam auxiliar nas atividades dos órgãos responsáveis pela vigilância epidemiológica e no desenvolvimento de políticas de prevenção e controle de doenças em animais. / The animals’ movements in a farms network and the spread of some animal diseases are intrinsically related. Therefore, comprehending the dynamics of the spreading of infectious diseases in these networks is an important tool in controlling these diseases. Using the information about the bovine movements from the State of Mato Grosso, Brazil, in 2007, we rebuilt the network of animal movements and the geographic proximity network between the premises of this state, in addition to hypothetical networks following the network models Molloy-Reed, Kalisky, Method A and Method B, where we simulated, using different configurations of the model SLIRS, the spread of diseases with hypothetical parameters e real ones (brucellosis and foot and mouth disease). Moreover, we simulated the control of these diseases spreading, considering the control by immunization and by restriction, with and without the rearrangement of the movements after the restriction, selecting the premises to be protected randomly, based on the degree of animal’s movements and using the concept of the friendship paradox. Among the results, stands out that although the pattern of the prevalence curves in the hypothetical networks were similar to the ones in the real network, the observed values were higher in the hypothetical networks, indicating that using them in the planning of policies to control diseases in place of the real network might lead to a greater expense of resources than it would be necessary. Furthermore, in the control of the diseases both with hypothetical parameters as well as with real parameters, in the simulations using only the animal’s movements network, it was observed a more effective reduction of the prevalence when selecting the premises with the highest total degree than the random selection, while in the simulations that considered the network of geographic proximity of the premises, the reduction in the prevalence of the strategies that selected specific premises were similar to the random selection. On the effect of rearranging the movements, it was observed that it may facilitate the spread of diseases in the network even in situations where some control strategy is used. We hope that the results of the mathematical simulations may contribute to the discussion of the relative impact of the mentioned control strategies and that in the future they may assist in the activities of agencies responsible for disease surveillance and in the development of policies to prevent and control diseases in animals.

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.

This dissertation employs management systems engineering principles, data science, and industrial systems engineering techniques to address pressing challenges in emergency department (ED) efficiency, infectious disease management at mass gatherings, and student self-efficacy. It is structured into three essays, each contributing to a distinct domain of research, and utilizes industrial and systems engineering approaches to provide data-driven insights and recommend solutions. The first essay used data analytics and regression analysis to understand how patient length of stay (LOS) in EDs could be influenced by multi-level variables integrating patient, service, and organizational factors. The findings suggested that specific demographic variables, the complexity of service provided, and staff-related variables significantly impacted LOS, offering guidance for operational improvements and better resource allocation. The second essay utilized system dynamics simulations to develop a modified SEIR model for modeling infectious diseases during mass gatherings and assessing the effectiveness of commonly implemented policies. The results demonstrated the significant collective impact of interventions such as visitor limits, vaccination mandates, and mask wearing, emphasizing their role in preventing health crises. The third essay applied machine learning methods to predict student self-efficacy in Muslim societies, revealing the importance of socio-emotional traits, cognitive abilities, and regulatory competencies. It provided a basis for identifying students with varying levels of self-efficacy and developing tailored strategies to enhance their academic and personal success. Collectively, these essays underscore the value of data-driven and evidence-based decision- making. The dissertation's broader impact lies in its contribution to optimizing healthcare operations, informing public health policy, and shaping educational strategies to be more culturally sensitive and psychologically informed. It provides a roadmap for future research and practical applications across the healthcare, public health, and education sectors, fostering advancements that could significantly benefit society. / Doctor of Philosophy / Divided into three essays, this dissertation uses industrial and systems engineering and data science to help make emergency departments more efficient, manage the spread of diseases at large events, and predict students' belief in their abilities. The first essay investigates factors that influence how long patients stay in emergency departments, including patient demographics, triage level, the complexity of care they receive, and number of emergency department staff when patient arrived. The essay offers suggestions to improve these services and better manage resources. The second essay models the spread of COVID-19 during the Hajj, a religious mass gathering, and evaluates the effectiveness of three safety measures: limiting the number of attendees, vaccinations, and wearing masks. This essay shows how different strategies can work together to prevent outbreaks. The third essay uses artificial intelligence and machine learning to understand what affects Muslim students' confidence in their abilities, focusing on emotional intelligence, thinking skills, and self-discipline. The findings could help to identify students who need extra support and to create more personalized programs that will help them succeed. Overall, this dissertation contributes to advancing industrial and systems engineering and data science knowledge by addressing complex issues in healthcare, public health, and education, leading to more informed decisions and better strategies. Its broader impact includes improving hospital operations, guiding public health decisions, and helping develop educational programs and interventions that consider cultural and psychological factors.

Na modelagem epidemiológica é importante descrever situações da vida real, neste caso baseados no artigo (Brockmann; Helbing, 2013) apresentamos um modelo da dinâmica da propagação de uma doença infecciosa numa população que está divida em regiões e entre alguma delas existe mobilidade, representados por uma rede. Com o fim de saber se dada uma origem em quanto tempo e quantos casos são esperados nas outras regiões, expomos a definição da distância efetiva e consequentemente os caminhos mais prováveis dependente da mobilidade que respondem as questões. A dengue é uma doença infecciosa que vem sendo uma preocupação no mundo, particularmente no Brasil é um problema de saúde pública. No ano 2012 o estado de Rio de Janeiro reportou 13 cidades com maior incidência nas quais 7 delas estão correlacionadas, porém não é estabelecido como foi a mobilidade para ter estes resultados. Nós propomos aplicamos o modelo mencionado, obtendo as probabilidades das viagens entre estas cidades. / In epidemiological modeling it is important to describe real-life situations, in this case based on the article (\\cite) we present a model of the dynamics of the spread of an infectious disease in a population that is divided into regions and between some of them there is mobility, represented by a network In order to know if given an origin in how long and how many cases are expected in the other regions, we present the definition of the effective distance and consequently the most likely paths depending on the mobility that answer the questions. Dengue is an infectious disease that has been a concern in the world, particularly in Brazil is a problem of public health. In the year $ 2012 $ the state of Rio de Janeiro reported 13 cities with higher incidence in which $ 7$ of them are correlated, but it is not established how mobility was to have these results. We propose apply the mentioned model, obtaining the probabilities of travel between these cities.

Na modelagem epidemiológica é importante descrever situações da vida real, neste caso baseados no artigo (Brockmann; Helbing, 2013) apresentamos um modelo da dinâmica da propagação de uma doença infecciosa numa população que está divida em regiões e entre alguma delas existe mobilidade, representados por uma rede. Com o fim de saber se dada uma origem em quanto tempo e quantos casos são esperados nas outras regiões, expomos a definição da distância efetiva e consequentemente os caminhos mais prováveis dependente da mobilidade que respondem as questões. A dengue é uma doença infecciosa que vem sendo uma preocupação no mundo, particularmente no Brasil é um problema de saúde pública. No ano 2012 o estado de Rio de Janeiro reportou 13 cidades com maior incidência nas quais 7 delas estão correlacionadas, porém não é estabelecido como foi a mobilidade para ter estes resultados. Nós propomos aplicamos o modelo mencionado, obtendo as probabilidades das viagens entre estas cidades. / In epidemiological modeling it is important to describe real-life situations, in this case based on the article (\\cite) we present a model of the dynamics of the spread of an infectious disease in a population that is divided into regions and between some of them there is mobility, represented by a network In order to know if given an origin in how long and how many cases are expected in the other regions, we present the definition of the effective distance and consequently the most likely paths depending on the mobility that answer the questions. Dengue is an infectious disease that has been a concern in the world, particularly in Brazil is a problem of public health. In the year $ 2012 $ the state of Rio de Janeiro reported 13 cities with higher incidence in which $ 7$ of them are correlated, but it is not established how mobility was to have these results. We propose apply the mentioned model, obtaining the probabilities of travel between these cities.

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