| EPSRC Reference: |
GR/M62723/01 |
| Title: |
MARKOV CHAIN MONTE CARLO METHODS FOR STOCHASTIC EPIDEMIC MODELS |
| Principal Investigator: |
Roberts, Professor G O |
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| Department: |
Mathematics and Statistics |
| Organisation: |
Lancaster University |
| Scheme: |
Standard Research (Pre-FEC) |
| Starts: |
01 February 2000 |
Ends: |
31 October 2003 |
Value (£): |
109,744
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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 epidemic models, stochastic epidemic models are capable of capturing truly random behaviour in observed epidemics. However their usefulness has been severely limited by their intractability, so that only the very simplest of models have been carefully analysed. Whereas mathematical understanding of many classes of stochastic epidemics is now fairly complete. However, inference for these models is generally difficult, and it is complicated by the inevitability that data is incomplete. Moreover, standard Markov chain Monte Carlo (MCMC) procedures are inadequate due to high dependence among unobserved components such as individual infection times. The aim of the proposed research is to develop efficient MCMC algorithms for analysing infection disease data using stochastic epidemic models in a Bayesian framework. Different specific types of models will be considered, including spatially homogeneous epidemics, small population (for instance household) epidemics, and network epidemic models. The emphasis will be on developing and applying non-standard MCMC methods, including multiple site update Langevin type methods, the so-called Hybrid Monte Carlo method, and auxiliary variable methods. Asymptotic probabilistic results will also be used to motivate and guide the construction of appropriate algorithms. All methods will be tested on real data examples.
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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.lancs.ac.uk |