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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

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Official URL: http://dx.doi.org/10.1016/j.jtbi.2019.109991

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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
Subjects: Q Science > QH Natural history
R Medicine > RA Public aspects of medicine
Divisions: Faculty of 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:
DateEvent
21 December 2019Published
2 September 2019Available
2 September 2019Accepted
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
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
UNSPECIFIED[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
EP/L015374/1[MRC] Medical Research Councilhttp://dx.doi.org/10.13039/501100000265

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