Statistical inference and modelling for nosocomial infections and the incorporation of whole genome sequence data
Healthcare-associated infections (HCAIs) remain a problem worldwide, and can cause severe illness and death. The increasing level of antibiotic resistance among bacteria that cause HCAIs limits infection treatment options, and is a major concern. Statistical modelling is a vital tool in developing a...
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
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2013
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| Online Access: | https://eprints.nottingham.ac.uk/13154/ |
| _version_ | 1848791665887674368 |
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| author | Worby, Colin J. |
| author_facet | Worby, Colin J. |
| author_sort | Worby, Colin J. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Healthcare-associated infections (HCAIs) remain a problem worldwide, and can cause severe illness and death. The increasing level of antibiotic resistance among bacteria that cause HCAIs limits infection treatment options, and is a major concern. Statistical modelling is a vital tool in developing an understanding of HCAI transmission dynamics. In this thesis, stochastic epidemic models are developed and used with the aim of investigating methicillin-resistant Staphylococcus aureus (MRSA) transmission and intervention measures in hospital wards.
A detailed analysis of MRSA transmission and the effectiveness of patient isolation was performed, using data collected from several general medical wards in London. A Markov chain Monte Carlo (MCMC) algorithm was used to derive parameter estimates, accounting for unobserved transmission dynamics. A clear reduction in transmission associated with the use of patient isolation was estimated.
A Bayesian framework offers considerable benefits and flexibility when dealing with missing data; however, model comparison is difficult, and existing methods are far from universally accepted. Two commonly used Bayesian model selection tools, reversible jump MCMC and the deviance information criterion (DIC), were thoroughly investigated in a transmission model setting, using both simulated and real data.
The collection of whole genome sequence (WGS) data is becoming easier, faster and cheaper than ever before. With WGS data likely to become abundant in the near future, the development of sophisticated analytical tools and models to exploit such genetic information is of great importance. New methods were developed to model MRSA transmission, using both genetic and epidemiological data, allowing for the reconstruction of transmission networks and simultaneous estimation of key transmission parameters. This approach was tested with simulated data and employed on WGS data collected from two Thai intensive care units. This work offers much scope for future investigations into genetic diversity and more complex transmission models, once suitable data become available. |
| first_indexed | 2025-11-14T18:32:08Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-13154 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:32:08Z |
| publishDate | 2013 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-131542025-02-28T11:23:29Z https://eprints.nottingham.ac.uk/13154/ Statistical inference and modelling for nosocomial infections and the incorporation of whole genome sequence data Worby, Colin J. Healthcare-associated infections (HCAIs) remain a problem worldwide, and can cause severe illness and death. The increasing level of antibiotic resistance among bacteria that cause HCAIs limits infection treatment options, and is a major concern. Statistical modelling is a vital tool in developing an understanding of HCAI transmission dynamics. In this thesis, stochastic epidemic models are developed and used with the aim of investigating methicillin-resistant Staphylococcus aureus (MRSA) transmission and intervention measures in hospital wards. A detailed analysis of MRSA transmission and the effectiveness of patient isolation was performed, using data collected from several general medical wards in London. A Markov chain Monte Carlo (MCMC) algorithm was used to derive parameter estimates, accounting for unobserved transmission dynamics. A clear reduction in transmission associated with the use of patient isolation was estimated. A Bayesian framework offers considerable benefits and flexibility when dealing with missing data; however, model comparison is difficult, and existing methods are far from universally accepted. Two commonly used Bayesian model selection tools, reversible jump MCMC and the deviance information criterion (DIC), were thoroughly investigated in a transmission model setting, using both simulated and real data. The collection of whole genome sequence (WGS) data is becoming easier, faster and cheaper than ever before. With WGS data likely to become abundant in the near future, the development of sophisticated analytical tools and models to exploit such genetic information is of great importance. New methods were developed to model MRSA transmission, using both genetic and epidemiological data, allowing for the reconstruction of transmission networks and simultaneous estimation of key transmission parameters. This approach was tested with simulated data and employed on WGS data collected from two Thai intensive care units. This work offers much scope for future investigations into genetic diversity and more complex transmission models, once suitable data become available. 2013-07-10 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en arr https://eprints.nottingham.ac.uk/13154/1/Thesis_Final_Mar2013.pdf Worby, Colin J. (2013) Statistical inference and modelling for nosocomial infections and the incorporation of whole genome sequence data. PhD thesis, University of Nottingham. |
| spellingShingle | Worby, Colin J. Statistical inference and modelling for nosocomial infections and the incorporation of whole genome sequence data |
| title | Statistical inference and modelling for nosocomial infections and the incorporation of whole genome sequence data |
| title_full | Statistical inference and modelling for nosocomial infections and the incorporation of whole genome sequence data |
| title_fullStr | Statistical inference and modelling for nosocomial infections and the incorporation of whole genome sequence data |
| title_full_unstemmed | Statistical inference and modelling for nosocomial infections and the incorporation of whole genome sequence data |
| title_short | Statistical inference and modelling for nosocomial infections and the incorporation of whole genome sequence data |
| title_sort | statistical inference and modelling for nosocomial infections and the incorporation of whole genome sequence data |
| url | https://eprints.nottingham.ac.uk/13154/ |