RT Journal Article
SR Electronic
T1 World Influence of Infectious Diseases from Wikipedia Network Analysis
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 424465
DO 10.1101/424465
A1 Guillaume Rollin
A1 José Lages
A1 Dima L. Shepelyansky
YR 2018
UL http://biorxiv.org/content/early/2018/09/24/424465.abstract
AB We consider the network of 5 416 537 articles of English Wikipedia extracted in 2017. Using the recent reduced Google matrix (REGOMAX) method we 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 we 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.