The severity of the outbreak of an infectious disease is highly dependent upon the structure of the population through which it spreads. This thesis considers the stochastic SIR (susceptible → infective → removed) household epidemic model, in which individuals mix with other individuals in their household at a far higher rate than with any other member of the population. This model gives a more realistic view of dynamics for the transmission of many diseases than the traditional model, in which all individuals in a population mix homogeneously, but retains mathematical tractability, allowing us to draw inferences from disease data. This thesis considers inference from epidemics using data which has been acquired after an outbreak has finished and whilst it is still in its early, `emerging' phase. An asymptotically unbiased method for estimating within household infectious contact rate(s) from emerging epidemic data is developed as well as hypothesis testing based on final size epidemic data. Finally, we investigate the use of both emerging and final size epidemic data to estimate the vaccination coverage required to prevent a large scale epidemic from occurring. Throughout the thesis we also consider the exact form of the households epidemic model which should be used. Specifically, we consider models in which the level of infectious contact between two individuals in the same household varies according to the size of their household.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:703261 |
| Date | January 2016 |
| Creators | Shaw, Laurence M. |
| Publisher | University of Nottingham |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://eprints.nottingham.ac.uk/38606/ |
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