Dynamics and control of diseases in networks with community structure.

The dynamics of infectious diseases spread via direct person-to-person transmission (such as influenza, smallpox, HIV/AIDS, etc.) depends on the underlying host contact network. Human contact networks exhibit strong community structure. Understanding how such community structure affects epidemics ma...

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Main Authors: Marcel Salathé, James H Jones
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2010-04-01
Series:PLoS Computational Biology
Online Access:http://europepmc.org/articles/PMC2851561?pdf=render
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spelling doaj-6ffd5e02940c45b29a0576484312cc0a2020-11-25T01:53:27ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582010-04-0164e100073610.1371/journal.pcbi.1000736Dynamics and control of diseases in networks with community structure.Marcel SalathéJames H JonesThe dynamics of infectious diseases spread via direct person-to-person transmission (such as influenza, smallpox, HIV/AIDS, etc.) depends on the underlying host contact network. Human contact networks exhibit strong community structure. Understanding how such community structure affects epidemics may provide insights for preventing the spread of disease between communities by changing the structure of the contact network through pharmaceutical or non-pharmaceutical interventions. We use empirical and simulated networks to investigate the spread of disease in networks with community structure. We find that community structure has a major impact on disease dynamics, and we show that in networks with strong community structure, immunization interventions targeted at individuals bridging communities are more effective than those simply targeting highly connected individuals. Because the structure of relevant contact networks is generally not known, and vaccine supply is often limited, there is great need for efficient vaccination algorithms that do not require full knowledge of the network. We developed an algorithm that acts only on locally available network information and is able to quickly identify targets for successful immunization intervention. The algorithm generally outperforms existing algorithms when vaccine supply is limited, particularly in networks with strong community structure. Understanding the spread of infectious diseases and designing optimal control strategies is a major goal of public health. Social networks show marked patterns of community structure, and our results, based on empirical and simulated data, demonstrate that community structure strongly affects disease dynamics. These results have implications for the design of control strategies.http://europepmc.org/articles/PMC2851561?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Marcel Salathé
James H Jones
spellingShingle Marcel Salathé
James H Jones
Dynamics and control of diseases in networks with community structure.
PLoS Computational Biology
author_facet Marcel Salathé
James H Jones
author_sort Marcel Salathé
title Dynamics and control of diseases in networks with community structure.
title_short Dynamics and control of diseases in networks with community structure.
title_full Dynamics and control of diseases in networks with community structure.
title_fullStr Dynamics and control of diseases in networks with community structure.
title_full_unstemmed Dynamics and control of diseases in networks with community structure.
title_sort dynamics and control of diseases in networks with community structure.
publisher Public Library of Science (PLoS)
series PLoS Computational Biology
issn 1553-734X
1553-7358
publishDate 2010-04-01
description The dynamics of infectious diseases spread via direct person-to-person transmission (such as influenza, smallpox, HIV/AIDS, etc.) depends on the underlying host contact network. Human contact networks exhibit strong community structure. Understanding how such community structure affects epidemics may provide insights for preventing the spread of disease between communities by changing the structure of the contact network through pharmaceutical or non-pharmaceutical interventions. We use empirical and simulated networks to investigate the spread of disease in networks with community structure. We find that community structure has a major impact on disease dynamics, and we show that in networks with strong community structure, immunization interventions targeted at individuals bridging communities are more effective than those simply targeting highly connected individuals. Because the structure of relevant contact networks is generally not known, and vaccine supply is often limited, there is great need for efficient vaccination algorithms that do not require full knowledge of the network. We developed an algorithm that acts only on locally available network information and is able to quickly identify targets for successful immunization intervention. The algorithm generally outperforms existing algorithms when vaccine supply is limited, particularly in networks with strong community structure. Understanding the spread of infectious diseases and designing optimal control strategies is a major goal of public health. Social networks show marked patterns of community structure, and our results, based on empirical and simulated data, demonstrate that community structure strongly affects disease dynamics. These results have implications for the design of control strategies.
url http://europepmc.org/articles/PMC2851561?pdf=render
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