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Mean-field models for non-Markovian epidemics on networks

This paper introduces a novel extension of the edge-based compartmental model to epidemics where the transmission and recovery processes are driven by general independent probability distributions. Edge-based compartmental modelling is just one of many different approaches used to model the spread o...

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Detalles Bibliográficos
Autores principales: Sherborne, Neil, Miller, Joel C., Blyuss, Konstantin B., Kiss, Istvan Z.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5772140/
https://www.ncbi.nlm.nih.gov/pubmed/28685365
http://dx.doi.org/10.1007/s00285-017-1155-0
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author Sherborne, Neil
Miller, Joel C.
Blyuss, Konstantin B.
Kiss, Istvan Z.
author_facet Sherborne, Neil
Miller, Joel C.
Blyuss, Konstantin B.
Kiss, Istvan Z.
author_sort Sherborne, Neil
collection PubMed
description This paper introduces a novel extension of the edge-based compartmental model to epidemics where the transmission and recovery processes are driven by general independent probability distributions. Edge-based compartmental modelling is just one of many different approaches used to model the spread of an infectious disease on a network; the major result of this paper is the rigorous proof that the edge-based compartmental model and the message passing models are equivalent for general independent transmission and recovery processes. This implies that the new model is exact on the ensemble of configuration model networks of infinite size. For the case of Markovian transmission the message passing model is re-parametrised into a pairwise-like model which is then used to derive many well-known pairwise models for regular networks, or when the infectious period is exponentially distributed or is of a fixed length.
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spelling pubmed-57721402018-01-30 Mean-field models for non-Markovian epidemics on networks Sherborne, Neil Miller, Joel C. Blyuss, Konstantin B. Kiss, Istvan Z. J Math Biol Article This paper introduces a novel extension of the edge-based compartmental model to epidemics where the transmission and recovery processes are driven by general independent probability distributions. Edge-based compartmental modelling is just one of many different approaches used to model the spread of an infectious disease on a network; the major result of this paper is the rigorous proof that the edge-based compartmental model and the message passing models are equivalent for general independent transmission and recovery processes. This implies that the new model is exact on the ensemble of configuration model networks of infinite size. For the case of Markovian transmission the message passing model is re-parametrised into a pairwise-like model which is then used to derive many well-known pairwise models for regular networks, or when the infectious period is exponentially distributed or is of a fixed length. Springer Berlin Heidelberg 2017-07-06 2018 /pmc/articles/PMC5772140/ /pubmed/28685365 http://dx.doi.org/10.1007/s00285-017-1155-0 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Article
Sherborne, Neil
Miller, Joel C.
Blyuss, Konstantin B.
Kiss, Istvan Z.
Mean-field models for non-Markovian epidemics on networks
title Mean-field models for non-Markovian epidemics on networks
title_full Mean-field models for non-Markovian epidemics on networks
title_fullStr Mean-field models for non-Markovian epidemics on networks
title_full_unstemmed Mean-field models for non-Markovian epidemics on networks
title_short Mean-field models for non-Markovian epidemics on networks
title_sort mean-field models for non-markovian epidemics on networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5772140/
https://www.ncbi.nlm.nih.gov/pubmed/28685365
http://dx.doi.org/10.1007/s00285-017-1155-0
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