Difference between revisions of "World Influence of Infectious Diseases from Wikipedia Network Analysis"

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== Overview ==
 
== Overview ==
 
Authors consider the network of 5 416 537 articles of [[English Wikipedia]] extracted in 2017. Using the recent reduced [[Google]] matrix (REGOMAX) method authors construct the reduced network of 230 articles (nodes) of infectious diseases and 195 articles of world countries. This method generates the reduced directed network between all 425 nodes taking into account all direct and indirect links with pathways via the huge global network. PageRank and CheiRank algorithms are used to determine the most influential diseases with the top PageRank diseases being Tuberculosis, HIV/AIDS and Malaria. From the reduced Google matrix authors determine the sensitivity of world countries to specific diseases integrating their influence over all their history including the times of ancient Egyptian mummies. The obtained results are compared with the World Health Organization (WHO) data demonstrating that the [[Wikipedia]] network analysis provides reliable results with up to about 80 percent overlap between WHO and REGOMAX analyses.
 
Authors consider the network of 5 416 537 articles of [[English Wikipedia]] extracted in 2017. Using the recent reduced [[Google]] matrix (REGOMAX) method authors construct the reduced network of 230 articles (nodes) of infectious diseases and 195 articles of world countries. This method generates the reduced directed network between all 425 nodes taking into account all direct and indirect links with pathways via the huge global network. PageRank and CheiRank algorithms are used to determine the most influential diseases with the top PageRank diseases being Tuberculosis, HIV/AIDS and Malaria. From the reduced Google matrix authors determine the sensitivity of world countries to specific diseases integrating their influence over all their history including the times of ancient Egyptian mummies. The obtained results are compared with the World Health Organization (WHO) data demonstrating that the [[Wikipedia]] network analysis provides reliable results with up to about 80 percent overlap between WHO and REGOMAX analyses.
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== Embed ==
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=== Wikipedia Quality ===
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Rollin, Guillaume; Lages, J.; Shepelyansky, Dima L.. (2018). "[[World Influence of Infectious Diseases from Wikipedia Network Analysis]]". Cold Spring Harbor Laboratory. DOI: 10.1101/424465.
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=== English Wikipedia ===
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{{cite journal |last1=Rollin |first1=Guillaume |last2=Lages |first2=J. |last3=Shepelyansky |first3=Dima L. |title=World Influence of Infectious Diseases from Wikipedia Network Analysis |date=2018 |doi=10.1101/424465 |url=https://wikipediaquality.com/wiki/World_Influence_of_Infectious_Diseases_from_Wikipedia_Network_Analysis |journal=Cold Spring Harbor Laboratory}}
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=== HTML ===
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Rollin, Guillaume; Lages, J.; Shepelyansky, Dima L.. (2018). &amp;quot;<a href="https://wikipediaquality.com/wiki/World_Influence_of_Infectious_Diseases_from_Wikipedia_Network_Analysis">World Influence of Infectious Diseases from Wikipedia Network Analysis</a>&amp;quot;. Cold Spring Harbor Laboratory. DOI: 10.1101/424465.
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Revision as of 08:34, 7 July 2020


World Influence of Infectious Diseases from Wikipedia Network Analysis
Authors
Guillaume Rollin
J. Lages
Dima L. Shepelyansky
Publication date
2018
DOI
10.1101/424465
Links
Original

World Influence of Infectious Diseases from Wikipedia Network Analysis - scientific work related to Wikipedia quality published in 2018, written by Guillaume Rollin, J. Lages and Dima L. Shepelyansky.

Overview

Authors consider the network of 5 416 537 articles of English Wikipedia extracted in 2017. Using the recent reduced Google matrix (REGOMAX) method authors construct the reduced network of 230 articles (nodes) of infectious diseases and 195 articles of world countries. This method generates the reduced directed network between all 425 nodes taking into account all direct and indirect links with pathways via the huge global network. PageRank and CheiRank algorithms are used to determine the most influential diseases with the top PageRank diseases being Tuberculosis, HIV/AIDS and Malaria. From the reduced Google matrix authors determine the sensitivity of world countries to specific diseases integrating their influence over all their history including the times of ancient Egyptian mummies. The obtained results are compared with the World Health Organization (WHO) data demonstrating that the Wikipedia network analysis provides reliable results with up to about 80 percent overlap between WHO and REGOMAX analyses.

Embed

Wikipedia Quality

Rollin, Guillaume; Lages, J.; Shepelyansky, Dima L.. (2018). "[[World Influence of Infectious Diseases from Wikipedia Network Analysis]]". Cold Spring Harbor Laboratory. DOI: 10.1101/424465.

English Wikipedia

{{cite journal |last1=Rollin |first1=Guillaume |last2=Lages |first2=J. |last3=Shepelyansky |first3=Dima L. |title=World Influence of Infectious Diseases from Wikipedia Network Analysis |date=2018 |doi=10.1101/424465 |url=https://wikipediaquality.com/wiki/World_Influence_of_Infectious_Diseases_from_Wikipedia_Network_Analysis |journal=Cold Spring Harbor Laboratory}}

HTML

Rollin, Guillaume; Lages, J.; Shepelyansky, Dima L.. (2018). &quot;<a href="https://wikipediaquality.com/wiki/World_Influence_of_Infectious_Diseases_from_Wikipedia_Network_Analysis">World Influence of Infectious Diseases from Wikipedia Network Analysis</a>&quot;. Cold Spring Harbor Laboratory. DOI: 10.1101/424465.