Overview
Topic status: We're looking for students to study this topic.
A stochastic model for transmissible infectious diseases (such as flu, SARS, or measles) is the so-called Susceptible Infected Recovered (SIR) model. This can be adapted to take into account of effects such as control mechanism like isolation. With infectious disease data it is possible to make inferences for rates in the models. Bayesian analyses can take into account both prior information about rates and that there are unobserved events such as the time of infection. Generally, the recovered times are observed. It is important to be able to distinguish between different models which represent different mechanisms for transmission. The project involves reviewing the methods for fitting models such as the SIR model and developing techniques for choosing between different models using likelihood and Bayesian methods. Some current approaches use information criteria such as BIC or DIC. The project will involve applying the methods to data such as those concerning the transmission of pathogens in hospitals.
- Study level
- Honours
- Supervisors
- QUT
- Organisational unit
Science and Engineering Faculty
- Research area
- Contact
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Please contact the supervisor.
Professor Tony Pettitt
