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Correlation between prevalence of endemic infections in metapopulation networks
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Meakin, Sophie R. (2019) Correlation between prevalence of endemic infections in metapopulation networks. PhD thesis, University of Warwick.
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WRAP_Theses_Meakin_2019.pdf - Submitted Version - Requires a PDF viewer. Download (4Mb) | Preview |
Official URL: http://webcat.warwick.ac.uk/record=b3492762~S15
Abstract
An ongoing challenge in metapopulation modelling of infectious diseases is how to infer the coupling, or level of interaction, between groups of individuals. The individual-level behaviour that determines the interactions between groups is highly complex, and good data on relevant interactions are not always readily available. Moreover, even with access to good data on relevant interactions, it is unclear how this should translate into a transmission parameter.
On the other hand, long-term data on disease incidence are often more widely available and can be used to estimate the correlation between infection prevalence in two interacting groups. In this thesis, we explore the relationship between the coupling and the correlation using two approximation methods from probability theory: moment closure approximations and diffusion approximations. We propose that this relationship can be used to infer the coupling from the observed infection incidence, even if the observations are limited in some way. We also use two methods to show how properties of the metapopulation network structure, such as degree and edge-density, affect the correlation.
| Item Type: | Thesis (PhD) | ||||
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| Subjects: | Q Science > QA Mathematics R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine |
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| Library of Congress Subject Headings (LCSH): | Communicable diseases -- Mathematical models, Communicable diseases -- Statistical methods, Social interaction -- Mathematical models, Stochastic models | ||||
| Official Date: | November 2019 | ||||
| Dates: |
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| Institution: | University of Warwick | ||||
| Theses Department: | Mathematics Institute | ||||
| Thesis Type: | PhD | ||||
| Publication Status: | Unpublished | ||||
| Supervisor(s)/Advisor: | Keeling, Matthew James | ||||
| Format of File: | |||||
| Extent: | viii, 167 leaves : illustrations (some colour) | ||||
| Language: | eng |
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