Submitted by Anna Patricia Pachas Manrique (annapamanrique@gmail.com) on 2017-02-23T20:20:56Z No. of bitstreams: 1 20_02 (1).pdf: 1460371 bytes, checksum: e98c8f4713680550a770e8efcc7ffa14 (MD5) / Approved for entry into archive by Janete de Oliveira Feitosa (janete.feitosa@fgv.br) on 2017-08-03T17:52:37Z (GMT) No. of bitstreams: 1 20_02 (1).pdf: 1460371 bytes, checksum: e98c8f4713680550a770e8efcc7ffa14 (MD5) / Made available in DSpace on 2017-08-18T14:34:31Z (GMT). No. of bitstreams: 1 20_02 (1).pdf: 1460371 bytes, checksum: e98c8f4713680550a770e8efcc7ffa14 (MD5) Previous issue date: 2016-08-29 / The speed and comprehensiveness global level with the pathogen has spread in recent years has drawn attention to the importance of the contact’s social network structure. In fact, the topology of the networks in which members of society interact has influenced the dynamics of epidemics. Studies have shown that pathogens when disiparem in scale-free networks have different effects when compared broadcast in random networks, such as the classic models. In these there were epidemic threshold, may somehow the health ministry have a control on the dissipation of diseases by applying certain measures such as vaccines. Already in models in which are considered the networks, specifically the free network scale, the threshold disappears. Thus, the epidemic threshold depends on the topology is required to include within this structure models Because of the importance of these networks, random networks and scalefree have been implemented along the epidemics of propagation models to check the epidemic threshold and the characteristic time, noting that the epidemic threshold disappears / A velocidade e a abrangência a nível mundial com que os agentes patogênicos tem se disseminado nos últimos anos tem chamado a atenção para a importância da estrutura da rede social de contato . De fato, a topologia das redes na qual os membros da sociedade interagem têm influenciado na dinâmica das epidemias.Estudos têm demostrado que os agentes patogênicos ao se dissiparem em redes livres de escala tem efeitos diferentes se comparado quando difundidos em redes aleatórias, como nos modelos clássicos. Nestes existiam limiar de epidemia ,podendo de alguma forma as entidades de saúde ter um controle sobre a dissipação das enfermidades , aplicando certas medidas como as vacinas por exemplo. Já nos modelos nos quais são consideradas as redes , especificamente a rede livre de escala,este limiar desaparece. Desta forma, o limiar de epidemia ao depender da topologia se faz necessário incluir esta estrutura dentro dos modelos epidemiológicos. Devido a importância destas redes , redes aleatórias e principalmente redes livres de escala foram implementadas junto a modelos de propagação de epidemias para verificar o limiar de epidemia e o tempo característico , verificando que o limiar de epidemia desaparece.

The primary objective of this dissertation is to utilise, adapt and extend current stochastic models and statistical inference techniques to describe the transmission of nosocomial pathogens, i.e. hospital-acquired pathogens, and multiply-resistant organisms within the hospital setting. The emergence of higher levels of antibiotic resistance is threatening the long term viability of current treatment options and placing greater emphasis on the use of infection control procedures. The relative importance and value of various infection control practices is often debated and there is a lack of quantitative evidence concerning their effectiveness. The methods developed in this dissertation are applied to data of methicillin-resistant Staphylococcus aureus occurrence in intensive care units to quantify the effectiveness of infection control procedures. Analysis of infectious disease or carriage data is complicated by dependencies within the data and partial observation of the transmission process. Dependencies within the data are inherent because the risk of colonisation depends on the number of other colonised individuals. The colonisation times, chain and duration are often not visible to the human eye making only partial observation of the transmission process possible. Within a hospital setting, routine surveillance monitoring permits knowledge of interval-censored colonisation times. However, consideration needs to be given to the possibility of false negative outcomes when relying on observations from routine surveillance monitoring. SI (Susceptible, Infected) models are commonly used to describe community epidemic processes and allow for any inherent dependencies. Statistical inference techniques, such as the expectation-maximisation (EM) algorithm and Markov chain Monte Carlo (MCMC) can be used to estimate the model parameters when only partial observation of the epidemic process is possible. These methods appear well suited for the analysis of hospital infectious disease data but need to be adapted for short patient stays through migration. This thesis focuses on the use of Bayesian statistics to explore the posterior distributions of the unknown parameters. MCMC techniques are introduced to overcome analytical intractability caused by partial observation of the epidemic process. Statistical issues such as model adequacy and MCMC convergence assessment are discussed throughout the thesis. The new methodology allows the quantification of the relative importance of different transmission routes and the benefits of hospital practices, in terms of changed transmission rates. Evidence-based decisions can therefore be made on the impact of infection control procedures which is otherwise difficult on the basis of clinical studies alone. The methods are applied to data describing the occurrence of methicillin-resistant Staphylococcus aureus within intensive care units in hospitals in Brisbane and London