O vírus da influenza tipo A (VIA) é um importante agente em rebanhos suínos ao redor do mundo. Alguns subtipos do vírus podem ser transmitidos entre espécies diferentes, como aves, homem e suínos, proporcionando o aumento de mutações genômicas e de novas cepas circulantes. Os suínos são considerados hospedeiros de "mixagem", uma vez que possuem receptores para cepas de aves, de humanos e suínos. A doença clínica em suínos é caracterizada por quadro respiratório agudo e brando, com duração de 5 a 7 dias. Em rebanhos de reprodutores – como as Unidade Produtoras de Leitões (UPL) – um surto epidêmico de influenza pode levar o estabelecimento de uma infecção endêmica com duração de semanas a meses, sem produção de sinais clínicos evidentes. Protocolos de biosseguridade vêm sendo incorporados e padronizados pelas agroindústrias, visando prevenir a introdução de doenças infecciosas em rebanhos. Entretanto, existem lacunas no conhecimento dos fatores associados à biosseguridade em rebanhos de matrizes suínas brasileiras e sua relação com doenças infecciosas. Por essa razão, um estudo transversal foi realizado para estimar a soroprevalência do VIA em matrizes de UPL e explorar práticas de biosseguridade associadas à presença de anticorpos contra o vírus da influenza. Ao todo, foram amostradas 404 matrizes em 21 granjas. O diagnóstico sorológico foi realizado pelo ELISA (protocolo in house). Todas as amostras positivas pelo ELISA foram testadas usando a inibição de hemaglutinação (IH) para diagnosticar a presença de H1N1pdm2009, H1N2 e H3N2 como subtipos de vírus influenza. As informações sobre práticas de biosseguridade foram obtidas através da aplicação de um questionário. A associação entre o resultado do diagnóstico do ELISA de cada uma das matrizes amostradas e as práticas de biosseguridade da propriedade foi feitas através de um modelo de Regressão de Poisson Robusta, estimando a Razão de Prevalência (RP) como medida da associação. A prevalência estimada de anticorpos anti-VIA nas matrizes foi de 63,9% (IC 95%: 55% - 72%), sendo que todas as granjas tiveram resultados positivos. A frequência dos subtipos nas matrizes usando IH foi 51,9% para H1N1, 27,8% H1N2 e 0,6% H3N2. Coinfecções entre H1N1 e H1N2 foram observadas em 19 granjas. As práticas de biosseguridade associadas significativamente com a presença de anticorpos (p-valor <0,05) foram a "presença de tela anti-pássaros" (RP = 0,75) e "local de aclimatação para leitoas" (RP = 0,57) como fatores protetivos e "reposição externa de leitoas" (RP = 1,38) como associada a uma maior prevalência do vírus da influenza suína. Foi possível verificar que a soroprevalência do VIA nas matrizes comerciais da população estudada é alta, indicando que os animais são frequentemente expostos ao patógeno, e que algumas medidas de biosseguridade estão associadas com a ocorrência da doença, fornecendo subsídios técnicos sobre a importância dos protocolos de biosseguridade para a promoção da saúde do plantel. / Influenza A virus (IAV) is an important infectious agent in pig herds across the globe. Some subtypes of this virus can be transmitted between different species, such as birds, human and pig, increasing genomic mutations and evolving of new circulating strains. Pigs are considered "mixed vessel" for influenza A, since they have cell receptors for birds, humans and pigs strains. The clinical disease in pigs is characterized by an acute and mild respiratory disease, lasting from 5 to 7 days. In breeding herds such as sow farms, an epidemic outbreak of influenza can lead to the establishment of an endemic infection lasting weeks to months without clinical signs. Biosecurity procedures were incorporated and standardized by the agroindustry in order to prevent both the introduction and dissemination of infectious diseases. However, there are gaps in the knowledge about what biosecurity factors are associated with infectious diseases in Brazilian herds. For this reason, a cross-sectional study was carried out to estimate IAV seroprevalence in sows and assess which biosecurity practices are associated with the prevalence of influenza virus antibodies. Four hundred forty-four sows were sampled from 21 farms. Serological assays were performed using an ELISA test (in-house protocol). All ELISA positive samples were tested using the hemagglutination inhibition test (HI) to identify the presence of H1N1pdm2009, H1N2 and H3N2 subtypes. Information of biosecurity practices was obtained through the application of a questionnaire. Association between ELISA diagnostic result of each sampled sow and biosecurity practices was assessed using a robust Poisson regression and the Prevalence Ratio (PR) was used as the measure of association. The estimated prevalence of anti-IAV antibodies in sows was 63.9% (95% CI: 55% - 72%), and all farms had at least one seropositive sow s. The frequency of subtypes using HI was 51.9% for H1N1, 27.8% for H1N2 and 0.6% for H3N2. Co-infections with H1N1 and H1N2 were observed in 19 farms. Biosecurity practices such as "presence of bird-proof" (PR = 0.75), and "presence of an acclimatization unit" (PR = 0.57), protective ones, and "external replacement of gilts" (PR = 1.38), which was positively associated with the IAV prevalence, were statistically significant in the final model (p-value <0.05). It was possible to verify that IAV seroprevalence is high and some biosecurity procedures were associated with the serologic status, offering technical subsidies about the importance of the biosecurity for the herd heath.

High blood pressure, also called hypertension is one of the most common worldwide diseases afflicting humans and is a major risk factor for stroke, myocardial infarction, vascular disease, and chronic kidney disease. If blood pressure is controlled and oscillations in the hemodynamic variables are reduced, patients experience fewer complications after surgery. In clinical practice, this is usually achieved using manual drug delivery. Given that different patients have different sensitivity and reaction time to drugs, determining manually the right drug infusion rates may be difficult. This is a problem where automatic drug delivery can provide a solution, especially if it is designed to adapt to variations in the patient’s conditions. This research work presents an investigation into the development of abnormal blood pressure (hypertension) controllers for postoperative patients. Control of the drugs infusion rates is used to simultaneously regulate the hemodynamic variables such as the Mean Arterial Pressure (MAP) and the Cardiac Output (CO) at the desired level. The implementation of optimal control system is very essential to improve the quality of patient care and also to reduce the workload of healthcare staff and costs. Many researchers have conducted studies earlier on modelling and/or control of abnormal blood pressure for postoperative patients. However, there are still many concerns about smooth transition of blood pressure without any side effect. The blood pressure is classified in two categories: high blood pressure (Hypertension) and low blood pressure (Hypotension). The hypertension often occurred after cardiac surgery, and the hypotension occurred during cardiac surgery. To achieve the optimal control solution for these abnormal blood pressures, many methods are proposed, one of the common methods is infusing the drug related to blood pressure to maintain it at the desired level. There are several kinds of vasodilating drugs such as Sodium Nitroprusside (SNP), Dopamine (DPM), Nitro-glycerine (NTG), and so on, which can be used to treat postoperative patients, also used for hypertensive emergencies to keep the blood pressure at safety level. A comparative performance of two types of algorithms has been presented in chapter four. These include the Internal Model Control (IMC), and Proportional-Integral-Derivative (PID) controller. The resulting controllers are implemented, tested and verified for three sensitivity patient response. SNP is used for all three patients’ situation in order to reduce the pressure smoothly and maintain it at the desire level. A Genetic Algorithms (GAs) optimization technique has been implemented to optimise the controllers’ parameters. A set of experiments are presented to demonstrate the merits and capabilities of the control algorithms. The simulation results in chapter four have demonstrated that the performance criteria are satisfied with the IMC, and PID controllers. On the other hand, the settling time for the PID control of all three patients’ response is shorter than the settling time with IMC controller. Using multiple interacting drugs to control both the MAP and CO of patients with different sensitivity to drugs is a challenging task. A Multivariable Model Reference Adaptive Control (MMRAC) algorithm is developed using a two-input, two-output patient model. Because of the difference in patient’s sensitivity to the drug, and in order to cover the wide ranges of patients, Model Reference Adaptive Control (MRAC) has been implemented to obtain the optimal infusion rates of DPM and SNP. This is developed in chapters five and six. Computer simulations were carried out to investigate the performance of this controller. The results show that the proposed adaptive scheme is robust with respect to disturbances and variations in model parameters, the simulation results have demonstrated that this algorithm cannot cover the wide range of patient’s sensitivity to drugs, due to that shortcoming, a PID controller using a Neural Network that tunes the controller parameters was designed and implemented. The parameters of the PID controller were optimised offline using Matlab genetic algorithm. The proposed Neuro-PID controller has been tested and validated to demonstrate its merits and capabilities compared to the existing approaches to cover wide range of patients.

High blood pressure, also called hypertension is one of the most common worldwide diseases afflicting humans and is a major risk factor for stroke, myocardial infarction, vascular disease, and chronic kidney disease. If blood pressure is controlled and oscillations in the hemodynamic variables are reduced, patients experience fewer complications after surgery. In clinical practice, this is usually achieved using manual drug delivery. Given that different patients have different sensitivity and reaction time to drugs, determining manually the right drug infusion rates may be difficult. This is a problem where automatic drug delivery can provide a solution, especially if it is designed to adapt to variations in the patient’s conditions. This research work presents an investigation into the development of abnormal blood pressure (hypertension) controllers for postoperative patients. Control of the drugs infusion rates is used to simultaneously regulate the hemodynamic variables such as the Mean Arterial Pressure (MAP) and the Cardiac Output (CO) at the desired level. The implementation of optimal control system is very essential to improve the quality of patient care and also to reduce the workload of healthcare staff and costs. Many researchers have conducted studies earlier on modelling and/or control of abnormal blood pressure for postoperative patients. However, there are still many concerns about smooth transition of blood pressure without any side effect. The blood pressure is classified in two categories: high blood pressure (Hypertension) and low blood pressure (Hypotension). The hypertension often occurred after cardiac surgery, and the hypotension occurred during cardiac surgery. To achieve the optimal control solution for these abnormal blood pressures, many methods are proposed, one of the common methods is infusing the drug related to blood pressure to maintain it at the desired level. There are several kinds of vasodilating drugs such as Sodium Nitroprusside (SNP), Dopamine (DPM), Nitro-glycerine (NTG), and so on, which can be used to treat postoperative patients, also used for hypertensive emergencies to keep the blood pressure at safety level. A comparative performance of two types of algorithms has been presented in chapter four. These include the Internal Model Control (IMC), and Proportional-Integral-Derivative (PID) controller. The resulting controllers are implemented, tested and verified for three sensitivity patient response. SNP is used for all three patients’ situation in order to reduce the pressure smoothly and maintain it at the desire level. A Genetic Algorithms (GAs) optimization technique has been implemented to optimise the controllers’ parameters. A set of experiments are presented to demonstrate the merits and capabilities of the control algorithms. The simulation results in chapter four have demonstrated that the performance criteria are satisfied with the IMC, and PID controllers. On the other hand, the settling time for the PID control of all three patients’ response is shorter than the settling time with IMC controller. Using multiple interacting drugs to control both the MAP and CO of patients with different sensitivity to drugs is a challenging task. A Multivariable Model Reference Adaptive Control (MMRAC) algorithm is developed using a two-input, two-output patient model. Because of the difference in patient’s sensitivity to the drug, and in order to cover the wide ranges of patients, Model Reference Adaptive Control (MRAC) has been implemented to obtain the optimal infusion rates of DPM and SNP. This is developed in chapters five and six. Computer simulations were carried out to investigate the performance of this controller. The results show that the proposed adaptive scheme is robust with respect to disturbances and variations in model parameters, the simulation results have demonstrated that this algorithm cannot cover the wide range of patient’s sensitivity to drugs, due to that shortcoming, a PID controller using a Neural Network that tunes the controller parameters was designed and implemented. The parameters of the PID controller were optimised offline using Matlab genetic algorithm. The proposed Neuro-PID controller has been tested and validated to demonstrate its merits and capabilities compared to the existing approaches to cover wide range of patients. / Libyan Ministry of Higher Education scholarship

La survie nette est un indicateur très utilisé en épidémiologie des cancers. Il s'agit de la survie que l'on observerait si la seule cause de mortalité était le cancer ; il est le seul indicateur épidémiologique utilisable à des fins de comparaisons de survie (entre périodes/pays) car il s'affranchit des éventuelles différences de mortalité dues aux autres causes que le cancer. Le premier objectif de notre travail était d'analyser les performances des différentes méthodes d'estimation de la survie nette sur données simulées ainsi que sur données réelles afin que les méthodes non biaisées soient reconnues scientifiquement et soient les seules à être utilisées par la suite. Nous avons ainsi démontré que deux approches étaient capables d'estimer sans biais la survie nette : l'approche non paramétrique de Pohar-Perme et l'approche reposant sur une modélisation multivariée du taux de mortalité en excès dû au cancer. Cette dernière approche impose une stratégie de construction difficile à mettre en place. Le deuxième objectif était de développer une boîte à outils composée de différents tests permettant de vérifier les différentes hypothèses faites lors de la construction d'un modèle de régression du taux de mortalité en excès. Ces hypothèses concernent habituellement la proportionnalité ou non de l'effet des covariables, leur forme fonctionnelle, ainsi que la fonction de lien utilisée. Le troisième objectif était une application épidémiologique qui visait à étudier l'impact des facteurs pronostiques, tel que le stade au diagnostic, sur la survie nette conditionnelle, en d'autres termes sur la dynamique du taux de mortalité en excès, après la survenue d'un cancer du côlon / Net survival is one of the most important indicators in cancer epidemiology. It is defined as the survival that would be observed if cancer were the only cause of death. This is the only one indicator allowing comparisons of cancer impact between countries or time periods because it is not influenced by death because of other causes. The first objective of this work was to compare the performance of several estimators of the net survival in a simulation study and then on real data in order to promote unbiased methods. Those methods are the non-parametric Pohar-Perme method and the parametric multivariable excess rate model. The latest one needs a model building strategy. The use of diagnostic procedures for model checking is an essential part of the modeling process. The second objective was to develop a tool box composed of diagnostic tools allowing to check hypothesis usually considered when constructing an excess mortality rate model, that is, the proportionality or not of the effect of covariates, their functional form and the link function. The third objective deals with the study of the impact of prognostic variables, such as stage at diagnosis, on conditional net survival, that is, on the dynamic of the excess hazard mortality after the diagnosis of colon cancer