Using network theory to identify the causes of disease outbreaks of unknown origin
2013; Royal Society; Volume: 10; Issue: 82 Linguagem: Inglês
10.1098/rsif.2013.0127
ISSN1742-5689
AutoresTiffany L. Bogich, Sebastian Funk, Trent R. Malcolm, Nok Chhun, Jonathan H. Epstein, Aleksei A. Chmura, A. Marm Kilpatrick, John S. Brownstein, O. Clyde Hutchison, Catherine Doyle‐Capitman, Robert Deaville, Stephen S. Morse, Andrew A. Cunningham, Peter Daszak,
Tópico(s)Bioinformatics and Genomic Networks
ResumoYou have accessMoreSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail Cite this article Bogich Tiffany L., Funk Sebastian, Malcolm Trent R., Chhun Nok, Epstein Jonathan H., Chmura Aleksei A., Kilpatrick A. Marm, Brownstein John S., Hutchison O. Clyde, Doyle-Capitman Catherine, Deaville Robert, Morse Stephen S., Cunningham Andrew A. and Daszak Peter 2013Using network theory to identify the causes of disease outbreaks of unknown originJ. R. Soc. Interface.102013012720130127http://doi.org/10.1098/rsif.2013.0127SectionYou have accessCorrectionsUsing network theory to identify the causes of disease outbreaks of unknown origin Tiffany L. Bogich Tiffany L. Bogich Google Scholar Find this author on PubMed Search for more papers by this author , Sebastian Funk Sebastian Funk Google Scholar Find this author on PubMed Search for more papers by this author , Trent R. Malcolm Trent R. Malcolm Google Scholar Find this author on PubMed Search for more papers by this author , Nok Chhun Nok Chhun Google Scholar Find this author on PubMed Search for more papers by this author , Jonathan H. Epstein Jonathan H. Epstein Google Scholar Find this author on PubMed Search for more papers by this author , Aleksei A. Chmura Aleksei A. Chmura Google Scholar Find this author on PubMed Search for more papers by this author , A. Marm Kilpatrick A. Marm Kilpatrick Google Scholar Find this author on PubMed Search for more papers by this author , John S. Brownstein John S. Brownstein Google Scholar Find this author on PubMed Search for more papers by this author , O. Clyde Hutchison O. Clyde Hutchison Google Scholar Find this author on PubMed Search for more papers by this author , Catherine Doyle-Capitman Catherine Doyle-Capitman Google Scholar Find this author on PubMed Search for more papers by this author , Robert Deaville Robert Deaville Google Scholar Find this author on PubMed Search for more papers by this author , Stephen S. Morse Stephen S. Morse Google Scholar Find this author on PubMed Search for more papers by this author , Andrew A. Cunningham Andrew A. Cunningham Google Scholar Find this author on PubMed Search for more papers by this author and Peter Daszak Peter Daszak Google Scholar Find this author on PubMed Search for more papers by this author Tiffany L. Bogich Tiffany L. Bogich Google Scholar Find this author on PubMed , Sebastian Funk Sebastian Funk Google Scholar Find this author on PubMed , Trent R. Malcolm Trent R. Malcolm Google Scholar Find this author on PubMed , Nok Chhun Nok Chhun Google Scholar Find this author on PubMed , Jonathan H. Epstein Jonathan H. Epstein Google Scholar Find this author on PubMed , Aleksei A. Chmura Aleksei A. Chmura Google Scholar Find this author on PubMed , A. Marm Kilpatrick A. Marm Kilpatrick Google Scholar Find this author on PubMed , John S. Brownstein John S. Brownstein Google Scholar Find this author on PubMed , O. Clyde Hutchison O. Clyde Hutchison Google Scholar Find this author on PubMed , Catherine Doyle-Capitman Catherine Doyle-Capitman Google Scholar Find this author on PubMed , Robert Deaville Robert Deaville Google Scholar Find this author on PubMed , Stephen S. Morse Stephen S. Morse Google Scholar Find this author on PubMed , Andrew A. Cunningham Andrew A. Cunningham Google Scholar Find this author on PubMed and Peter Daszak Peter Daszak Google Scholar Find this author on PubMed Published:06 May 2013https://doi.org/10.1098/rsif.2013.0127This article corrects the followingResearch ArticleUsing network theory to identify the causes of disease outbreaks of unknown originhttps://doi.org/10.1098/rsif.2012.0904 Tiffany L. Bogich, Sebastian Funk, Trent R. Malcolm, Nok Chhun, Jonathan H. Epstein, Aleksei A. Chmura, A. Marm Kilpatrick, John S. Brownstein, O. Clyde Hutchison, Catherine Doyle-Capitman, Robert Deaville, Stephen S. Morse, Andrew A. Cunningham and Peter Daszak volume 10issue 81Journal of The Royal Society Interface06 April 2013J. R. Soc. Interface10, 20120904 (2013; Published online 6 February 2013) (doi:10.1098/rsif.2012.0904)Figure 2 was presented incorrectly. The grey lines between clusters were missing—please find the correct figure below. Figure 2. Visualization of the network of diagnosed outbreaks of diseases with the potential to cause encephalitis (coloured) and outbreaks of undiagnosed encephalitis (white). The inner network describes the strength and relationship of individual outbreaks to each other, while the outer ring gives the composition of the seven communities of disease that are found by the community detection algorithm. Outbreaks of the same disease (colour) tend to cluster together. The network model acts to minimize the number of edges between outbreaks in different communities of disease and maximize the number of edges between outbreaks within a single community of disease. Each circle, called a 'node', represents a single outbreak report. Lines connecting two nodes indicate shared traits between two outbreak reports, in symptoms reported, the case fatality ratio or seasonality. Lines connecting two outbreaks within a single community are in black, and lines between two outbreaks in different communities are in grey. Thicker lines represent a greater number of shared traits, and thinner lines indicate fewer shared traits. Where nodes overlap, they are strongly connected. The size of a node (circle) representing an outbreak is proportional to the sum over the thicknesses of all edges connected to it, which can be interpreted as the amount of information contained in the outbreak report. Note that in all figures, lengths of edges and positions of nodes have no meaning as such and have been chosen based on an algorithm for optimal visualization [24].Download figureOpen in new tabDownload PowerPoint Previous Article VIEW FULL TEXT DOWNLOAD PDF FiguresRelatedReferencesDetailsCited by Edmunds K, Hunter P, Few R, Bell D and Tang J (2013) Hazard Analysis of Critical Control Points Assessment as a Tool to Respond to Emerging Infectious Disease Outbreaks, PLoS ONE, 10.1371/journal.pone.0072279, 8:8, (e72279) Related articlesUsing network theory to identify the causes of disease outbreaks of unknown origin06 April 2013Journal of The Royal Society Interface This Issue06 May 2013Volume 10Issue 82 Article InformationDOI:https://doi.org/10.1098/rsif.2013.0127Published by:Royal SocietyOnline ISSN:1742-5662History: Published online06/05/2013Published in print06/05/2013 License:© 2013 The Author(s) Published by the Royal Society. All rights reserved. Citations and impact
Referência(s)