bioRxivMicrobiology
The spatial-temporal dynamics of respiratory syncytial virus infections across the east-west coasts of Australia during 2016-17
M. Robertson, J.-S. Eden, A. Levy, I. Carter, R. L. Tulloch, E. J. Cutmore, B. A. Horsburgh, C. T. Sikazwe, D. E. Dwyer, D. W. Smith, J. Kok
Abstract
Respiratory syncytial virus (RSV) is an important human respiratory pathogen. In temperate regions a distinct seasonality is observed, where peaks of infections typically occur in early winter, often preceding the annual influenza season. Infections are associated with high rates of morbidity and mortality, and in some populations exceeds that of influenza. Two subtypes, RSV-A and RSV-B, have been described, and molecular epidemiological studies have shown that both viruses mostly co-circulate. This trend also appears to be the case for Australia, however previous genomic studies have been limited to cases from one Eastern state - New South Wales. As such, the broader spatial patterns and viral traffic networks across the continent are not known. Here, we conducted a whole genome study of RSV comparing strains across eastern and western Australia during the period January 2016 to June 2017. In total, 96 new RSV genomes were sequenced, compiled with previously generated data, and examined using a phylodynamic approach. This analysis revealed that both RSV-A and RSV-B strains were circulating, and each subtype was dominated by a single genotype, RSV-A/ON1-like and RSV-B/BA10-like viruses. Some geographical clustering was evident in strains from both states with multiple distinct sub-lineages observed and relatively low mixing across jurisdictions suggesting that endemic transmission was likely seeded from imported, unsampled locations. Overall, the RSV phylogenies reflected a complex pattern of interactions across multiple epidemiological scales from fluid virus traffic across global and regional networks to fine-scale local transmission events.
- organism: human
- assay: whole-genome sequencing
- research focus: phylogenetics / evolution
- approach: statistical model
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