Inferring influenza global transmission networks without complete phylogenetic information
Article first published online: 2 JAN 2014
© 2013 The Authors. Evolutionary Applications published by John Wiley & Sons Ltd.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
How to Cite
Aris-Brosou, S. (2014), Inferring influenza global transmission networks without complete phylogenetic information. Evolutionary Applications. doi: 10.1111/eva.12138
- Article first published online: 2 JAN 2014
- Manuscript Accepted: 6 NOV 2013
- Manuscript Received: 4 JUL 2013
- Natural Sciences Research Council of Canada
- Canada Foundation for Innovation
- genetic diversity;
- global dynamics;
- PageRank algorithm;
- transmission network
Influenza is one of the most severe respiratory infections affecting humans throughout the world, yet the dynamics of its global transmission network are still contentious. Here, I describe a novel combination of phylogenetics, time series, and graph theory to analyze 14.25 years of data stratified in space and in time, focusing on the main target of the human immune response, the hemagglutinin gene. While bypassing the complete phylogenetic inference of huge data sets, the method still extracts information suggesting that waves of genetic or of nucleotide diversity circulate continuously around the globe for subtypes that undergo sustained transmission over several seasons, such as H3N2 and pandemic H1N1/09, while diversity of prepandemic H1N1 viruses had until 2009 a noncontinuous transmission pattern consistent with a source/sink model. Irrespective of the shift in the structure of H1N1 diversity circulation with the emergence of the pandemic H1N1/09 strain, US prevalence peaks during the winter months when genetic diversity is at its lowest. This suggests that a dominant strain is generally responsible for epidemics and that monitoring genetic and/or nucleotide diversity in real time could provide public health agencies with an indirect estimate of prevalence.