Infectious disease models can be used to understand mechanisms of the spread of diseases and thus, may effectively guide control policies for potential outbreaks. Deardon et al. (2010) introduced a class of individual-level models (ILMs) which are highly flexible. Parameter estimates for ILMs can be achieved by means of Markov chain Monte Carlo (MCMC) methods within a Bayesian framework. Here, we introduce an extended form of ILM, described by
Deardon et al. (2010), and compare this model with the original ILM in the context of a simple
spatial system. The two spatial ILMs are fitted to 70 simulated data sets and a real data set on
tomato spotted wilt virus (TSWV) in pepper plants (Hughes et al., 1997). We find that the
modified ILM is more flexible than the original ILM and may fit some data sets better.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OGU.10214/3016 |
| Date | 15 September 2011 |
| Creators | Fang, Mingying |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0022 seconds