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Correlations between stochastic endemic infection in multiple interacting subpopulations
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Meakin, Sophie R. and Keeling, Matthew James (2019) Correlations between stochastic endemic infection in multiple interacting subpopulations. Journal of Theoretical Biology, 483 . 109991. doi:10.1016/j.jtbi.2019.109991 ISSN 0022-5193.
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Official URL: http://dx.doi.org/10.1016/j.jtbi.2019.109991
Abstract
Heterogeneity plays an important role in the emergence, persistence and control of infectious diseases. Metapopulation models are often used to describe spatial heterogeneity, and the transition from random- to heterogeneous-mixing is made by incorporating the interaction, or coupling, within and between subpopulations. However, such couplings are difficult to measure explicitly; instead, their action through the correlations between subpopulations is often all that can be observed. We use moment-closure methods to investigate how the coupling and resulting correlation are related, considering systems of multiple identical interacting populations on highly symmetric complex networks: the complete network, the k-regular tree network, and the star network. We show that the correlation between the prevalence of infection takes a relatively simple form and can be written in terms of the coupling, network parameters and epidemiological parameters only. These results provide insight into the effect of metapopulation network structure on endemic disease dynamics, and suggest that detailed case-reporting data alone may be sufficient to infer the strength of between population interaction and hence lead to more accurate mathematical descriptions of infectious disease behaviour.
Item Type: | Journal Article | |||||||||
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Subjects: | Q Science > QH Natural history R Medicine > RA Public aspects of medicine |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) | |||||||||
Library of Congress Subject Headings (LCSH): | Epidemiology -- Mathematical models, Population biology -- Mathematical models, Spatial systems | |||||||||
Journal or Publication Title: | Journal of Theoretical Biology | |||||||||
Publisher: | Elsevier | |||||||||
ISSN: | 0022-5193 | |||||||||
Official Date: | 21 December 2019 | |||||||||
Dates: |
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Volume: | 483 | |||||||||
Article Number: | 109991 | |||||||||
DOI: | 10.1016/j.jtbi.2019.109991 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||
Date of first compliant deposit: | 5 November 2019 | |||||||||
Date of first compliant Open Access: | 8 November 2019 | |||||||||
RIOXX Funder/Project Grant: |
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