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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Bibliographic Details
Published in:PLoS computational biology 2013-12, Vol.9 (12), p.e1003352-e1003352
Main Authors: Kamp, Christel, Moslonka-Lefebvre, Mathieu, Alizon, Samuel
Format: Article
Language:English
Subjects:
Behavior
Biodiversity
Biological research
Biology
Biology, Experimental
Computer Science
Disease
Disease Outbreaks
Disease transmission
Engineering
Epidemiologic Studies
Epidemiology
Humans
Life Sciences
Medicine
Microbiology and Parasitology
Modeling and Simulation
Models, Theoretical
Populations and Evolution
Sexually Transmitted Diseases - epidemiology
Social and Behavioral Sciences
Studies
Systematics, Phylogenetics and taxonomy
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Summary: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.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1003352