@phdthesis{quteprints103762,
          school = {Queensland University of Technology},
           title = {Statistical and simulation modelling for enhanced understanding of hospital pathogen and related health issues},
          author = {Xing Ju Lee},
            year = {2017},
             url = {https://eprints.qut.edu.au/103762/},
        keywords = {approximate Bayesian computation, data-augmentation, environmental contamination, Markov chain Monte Carlo, max-stable models, methicillin-resistant Staphylococcus aureus, model choice, non-homogeneous Poisson process},
        abstract = {This thesis investigated the temporal occurrence and transmission of within hospital pathogens using appropriate statistical and simulation models applied to imperfect hospital data. The research provides new insights into the transmission dynamics of methicillin-resistant Staphylococcus aureus within a hospital ward to assist infection control and prevention efforts. Additionally, appropriate statistical methods are identified to analyse hospital infection data which take into account the intricacies and potential limitations of such data.},
             doi = {10.5204/thesis.eprints.103762}
}