bioRxiv preprint doi: https://doi.org/10.1101/424465; this version posted September 24, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Manuscript SUBMITTED TO eLife WORLD INflUENCE OF INFECTIOUS 1 Diseases FROM Wikipedia Network 2 Analysis 3 1 1* 2* 4 Guillaume Rollin , José Lages , Dima L. Shepelyansky *For CORRespondence: INSTITUT UTINAM, ObservatoirE DES Sciences DE L’Univers THETA, CNRS, Université DE
[email protected] (JL); 5 1
[email protected] (DLS) BourGOGNE Franche-Comté, 25030 Besançon, France; LaborATOIRE DE Physique 6 2 Théorique, IRSAMC, Université DE Toulouse, CNRS, UPS, 31062 Toulouse, FrANCE 7 8 9 AbstrACT WE CONSIDER THE NETWORK OF 5 416 537 ARTICLES OF English Wikipedia EXTRACTED IN 2017. 10 Using THE RECENT REDUCED Google MATRIX (REGOMAX) METHOD WE CONSTRUCT THE REDUCED NETWORK OF 11 230 ARTICLES (nodes) OF INFECTIOUS DISEASES AND 195 ARTICLES OF WORLD countries. This METHOD 12 GENERATES THE REDUCED DIRECTED NETWORK BETWEEN ALL 425 NODES TAKING INTO ACCOUNT ALL DIRECT AND 13 INDIRECT LINKS WITH PATHWAYS VIA THE HUGE GLOBAL network. PageRank AND CheiRank ALGORITHMS ARE 14 USED TO DETERMINE THE MOST INflUENTIAL DISEASES WITH THE TOP PageRank DISEASES BEING TUBERculosis, 15 HIV/AIDS AND Malaria. FrOM THE REDUCED Google MATRIX WE DETERMINE THE SENSITIVITY OF WORLD 16 COUNTRIES TO SPECIfiC DISEASES INTEGRATING THEIR INflUENCE OVER ALL THEIR HISTORY INCLUDING THE TIMES OF 17 ANCIENT Egyptian mummies.