Epidemic spread on weighted networks.

The contact structure between hosts shapes disease spread. Most network-based models used in epidemiology tend to ignore heterogeneity in the weighting of contacts between two individuals. However, this assumption is known to be at odds with the data for many networks (e.g. sexual contact networks)...

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Autores principales:	Christel Kamp, Mathieu Moslonka-Lefebvre, Samuel Alizon
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/7d8bbf795e604f839816038115c31f74
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id	oai:doaj.org-article:7d8bbf795e604f839816038115c31f74
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spelling	oai:doaj.org-article:7d8bbf795e604f839816038115c31f742021-11-18T05:53:18ZEpidemic spread on weighted networks.1553-734X1553-735810.1371/journal.pcbi.1003352https://doaj.org/article/7d8bbf795e604f839816038115c31f742013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24348225/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The contact structure between hosts shapes disease spread. Most network-based models used in epidemiology tend to ignore heterogeneity in the weighting of contacts between two individuals. However, this assumption is known to be at odds with the data for many networks (e.g. sexual contact networks) and to have a critical influence on epidemics' behavior. One of the reasons why models usually ignore heterogeneity in transmission is that we currently lack tools to analyze weighted networks, such that most studies rely on numerical simulations. Here, we present a novel framework to estimate key epidemiological variables, such as the rate of early epidemic expansion (r0) and the basic reproductive ratio (R0), from joint probability distributions of number of partners (contacts) and number of interaction events through which contacts are weighted. These distributions are much easier to infer than the exact shape of the network, which makes the approach widely applicable. The framework also allows for a derivation of the full time course of epidemic prevalence and contact behaviour, which we validate with numerical simulations on networks. Overall, incorporating more realistic contact networks into epidemiological models can improve our understanding of the emergence and spread of infectious diseases.Christel KampMathieu Moslonka-LefebvreSamuel AlizonPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 12, p e1003352 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Christel Kamp Mathieu Moslonka-Lefebvre Samuel Alizon Epidemic spread on weighted networks.
description	The contact structure between hosts shapes disease spread. Most network-based models used in epidemiology tend to ignore heterogeneity in the weighting of contacts between two individuals. However, this assumption is known to be at odds with the data for many networks (e.g. sexual contact networks) and to have a critical influence on epidemics' behavior. One of the reasons why models usually ignore heterogeneity in transmission is that we currently lack tools to analyze weighted networks, such that most studies rely on numerical simulations. Here, we present a novel framework to estimate key epidemiological variables, such as the rate of early epidemic expansion (r0) and the basic reproductive ratio (R0), from joint probability distributions of number of partners (contacts) and number of interaction events through which contacts are weighted. These distributions are much easier to infer than the exact shape of the network, which makes the approach widely applicable. The framework also allows for a derivation of the full time course of epidemic prevalence and contact behaviour, which we validate with numerical simulations on networks. Overall, incorporating more realistic contact networks into epidemiological models can improve our understanding of the emergence and spread of infectious diseases.
format	article
author	Christel Kamp Mathieu Moslonka-Lefebvre Samuel Alizon
author_facet	Christel Kamp Mathieu Moslonka-Lefebvre Samuel Alizon
author_sort	Christel Kamp
title	Epidemic spread on weighted networks.
title_short	Epidemic spread on weighted networks.
title_full	Epidemic spread on weighted networks.
title_fullStr	Epidemic spread on weighted networks.
title_full_unstemmed	Epidemic spread on weighted networks.
title_sort	epidemic spread on weighted networks.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/7d8bbf795e604f839816038115c31f74
work_keys_str_mv	AT christelkamp epidemicspreadonweightednetworks AT mathieumoslonkalefebvre epidemicspreadonweightednetworks AT samuelalizon epidemicspreadonweightednetworks
_version_	1718424676346101760

Epidemic spread on weighted networks.

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