| EPSRC Reference: |
GR/M86323/01 |
| Title: |
MARKOV CHAIN MONTE CARLO METHODS FOR STOCHASTIC EPIDEMIC MODELS |
| Principal Investigator: |
O'Neill, Professor PD |
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| Department: |
Sch of Mathematical Sciences |
| Organisation: |
University of Nottingham |
| Scheme: |
Standard Research (Pre-FEC) |
| Starts: |
10 February 2000 |
Ends: |
09 February 2003 |
Value (£): |
44,182
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| EPSRC Research Topic Classifications: |
| Medical science & disease |
Statistics & Appl. Probability |
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| EPSRC Industrial Sector Classifications: |
| Healthcare |
No relevance to Underpinning Sectors |
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Summary on Grant Application Form |
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Unlike commonly-used deterministic models, stochastic epidemic models are capable of capturing the random behaviour in observed epidemics. However, their usefulness has been severely limited by the high level of intractability, so that only relatively simple models can be fully analysed. Althoug certain amount of progress has been made in this direction, the task of performing statistical infe for such models remains difficult, and is complicated by the inevitability that the data are incomp Moreover, standard Markov chain Monte Carlo (MCMC) prodedures are often inadequate due to high dependence among unobserved components such as individual infection times. The aim of the proposed research is to develop efficient Markov chain Monte Carlo (MCMC) algorithms for analysing infectiou disease data using stochastic models in a Bayesian framework. Different specific types of models % be considered, including spatially homogeneous epidemics, small population (eg household) epidemics network epidemic models. The emphasis will be on developing and applying non-standard MCMC algorit including multiple site update Langevin type methods, the so-called Hybrid Monte Carlo method, and auxilliary variable methods. Asymptotic probabilistic results will also be used to motivate and gu the construction of appropriate algorithms. All methods will be tested on real data.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.nottingham.ac.uk |