Mixing patterns in a global influenza a virus network using whole genome comparisons

Abstract

Abstract-Approximating 'real' disease transmission networks through genomic sequence comparisons among pathogenic isolates is increasingly feasible with the current growth in genomic sequence data. Here, we derive a network from over 4,200 globally distributed influenza A virus isolates based on alignment-free sequence comparisons. We then employ network mixing pattern analysis to examine transmission probabilities between isolates from different global regions, host types, subtypes and collection years. While we can not use our results to describe the complete global network of influenza A virus, we present a novel analytical process. In addition, we describe some of the characteristics of this subset of currently available data. Most notable results are the high levels of inter regional links and the important role that avian species seem to play in non human global transmission.

Complex Network Analysis of Research Funding: A Case Study of NSF Grants

“…Newman provided the first study on co-authorship networks by analyzing the macroscopic properties of different domains [6,7]. Similarly, researchers have studied academic ties [8], air transport [9], authors network [10], citation networks [11,12], friend recommendation [13], influenza spread [14,15], Internet topology [16][17][18], news networks [19,20], patent networks [21,22], protein interactions [23], software collaborations [24,25], and video industry [26] as complex networks.…”