Statistical approaches to revealing structure in complex health data
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Description
This thesis develops approaches for the modelling and analysis of structured data, considering two case study datasets containing features such as missing data, irregular time periods, workplace grouping, and spatial observations from different spatial scales. The models developed helped to: create individual health risk profiles over time, obtain ideal locations of health facilities to maximise their coverage, evaluate the impact of health facility access. Overall this thesis makes substantive contributions that extend models to account for data structures, provide corresponding new software tools, improve health surveillance and health resource usage, in the hope of improving health of the public.
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| ID Code: | 115536 |
|---|---|
| Item Type: | QUT Thesis (PhD by Publication) |
| Supervisor: | Mengersen, Kerrie & Harden, Fiona |
| Keywords: | Occupational Health Surveillance, Public Health Surveillance, Applied Statistics, Statistics, Bayesian Statistics, Decision Trees, Missing Data, Linear Programming, R, Automated External Defibrillator |
| DOI: | 10.5204/thesis.eprints.115536 |
| Divisions: | Past > QUT Faculties & Divisions > Science & Engineering Faculty Current > Schools > School of Mathematical Sciences |
| Institution: | Queensland University of Technology |
| Deposited On: | 09 Mar 2018 06:41 |
| Last Modified: | 09 Mar 2018 06:41 |
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