network analysis of global influenza spread全球流感传播的网络分析.pdfVIP

Network Analysis of Global Influenza Spread Joseph Chan*, Antony Holmes, Raul Rabadan Department of Biomedical Informatics and Center for Computational Biology and Bioinformatics, Columbia University College of Physicians and Surgeons, New York, New York, United States of America Abstract Although vaccines pose the best means of preventing influenza infection, strain selection and optimal implementation remain difficult due to antigenic drift and a lack of understanding global spread. Detecting viral movement by sequence analysis is complicated by skewed geographic and seasonal distributions in viral isolates. We propose a probabilistic method that accounts for sampling bias through spatiotemporal clustering and modeling regional and seasonal transmission as a binomial process. Analysis of H3N2 not only confirmed East-Southeast Asia as a source of new seasonal variants, but also increased the resolution of observed transmission to a country level. H1N1 data revealed similar viral spread from the tropics. Network analysis suggested China and Hong Kong as the origins of new seasonal H3N2 strains and the United States as a region where increased vaccination would maximally disrupt global spread of the virus. These techniques provide a promising methodology for the analysis of any seasonal virus, as well as for the continued surveillance of influenza. Citation: Chan J, Holmes A, Rabadan R (2010) Network Analysis of Global Influenza Spread. PLoS Comput Biol 6(11): e1001005. doi:10.1371/journal.pcbi.1001005 Editor: Sergei L. Kosakovsky Pond, University of California San Diego, United States of America Received June 22, 2010; Accepted October 21, 2010; Published November 18, 2010 Copyright: 2010 Chan et al. This is an open-access article distributed under the terms