Dataset Information

Network analysis of global influenza spread.

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.

SUBMITTER: Chan J

PROVIDER: S-EPMC2987833 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature

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Network analysis of global influenza spread.

Chan Joseph J Holmes Antony A Rabadan Raul R

PLoS computational biology 20101118 11

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. A ...[more]

PMID: 21124942

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Project description:The diversity of influenza A viruses (IAV) is primarily hosted by two highly divergent avian orders: Anseriformes (ducks, swans and geese) and Charadriiformes (gulls, terns and shorebirds). Studies of IAV have historically focused on Anseriformes, specifically dabbling ducks, overlooking the diversity of hosts in nature, including gull and goose species that have successfully adapted to human habitats. This study sought to address this imbalance by characterizing spillover dynamics and global transmission patterns of IAV over 10 years at greater taxonomic resolution than previously considered. Furthermore, the circulation of viral subtypes in birds that are either host-adapted (low pathogenic H13, H16) or host-generalist (highly pathogenic avian influenza-HPAI H5) provided a unique opportunity to test and extend models of viral evolution. Using Bayesian phylodynamic modelling we uncovered a complex transmission network that relied on ecologically divergent bird hosts. The generalist subtype, HPAI H5 was driven largely by wild geese and swans that acted as a source for wild ducks, gulls, land birds, and domestic geese. Gulls were responsible for moving HPAI H5 more rapidly than any other host, a finding that may reflect their long-distance, pelagic movements and their immuno-naïve status against this subtype. Wild ducks, long viewed as primary hosts for spillover, occupied an optimal space for viral transmission, contributing to geographic expansion and rapid dispersal of HPAI H5. Evidence of inter-hemispheric dispersal via both the Pacific and Atlantic Rims was detected, supporting surveillance at high latitudes along continental margins to achieve early detection. Both neutral (geographic expansion) and non-neutral (antigenic selection) evolutionary processes were found to shape subtype evolution which manifested as unique geographic hotspots for each subtype at the global scale. This study reveals how a diversity of avian hosts contribute to viral spread and spillover with the potential to improve surveillance in an era of rapid global change.

Project description:BACKGROUND: The Global Influenza Hospital Surveillance Network (GIHSN) was developed to improve understanding of severe influenza infection, as represented by hospitalized cases. The GIHSN is composed of coordinating sites, mainly affiliated with health authorities, each of which supervises and compiles data from one to seven hospitals. This report describes the distribution of influenza viruses A(H1N1), A(H3N2), B/Victoria, and B/Yamagata resulting in hospitalization during 2012-2013, the network's first year. METHODS: In 2012-2013, the GIHSN included 21 hospitals (five in Spain, five in France, four in the Russian Federation, and seven in Turkey). All hospitals used a reference protocol and core questionnaire to collect data, and data were consolidated at five coordinating sites. Influenza infection was confirmed by reverse-transcription polymerase chain reaction. Hospitalized patients admitted within 7 days of onset of influenza-like illness were included in the analysis. RESULTS: Of 5034 patients included with polymerase chain reaction results, 1545 (30.7%) were positive for influenza. Influenza A(H1N1), A(H3N2), and both B lineages co-circulated, although distributions varied greatly between coordinating sites and over time. All age groups were affected. A(H1N1) was the most common influenza strain isolated among hospitalized adults 18-64 years of age at four of five coordinating sites, whereas A(H3N2) and B viruses were isolated more often than A(H1N1) in adults ?65 years of age at all five coordinating sites. A total of 16 deaths and 20 intensive care unit admissions were recorded among patients with influenza. CONCLUSIONS: Influenza strains resulting in hospitalization varied greatly between coordinating sites and over time. These first-year results of the GIHSN are relevant, useful, and timely. Due to its broad regional representativeness and sustainable framework, this growing network should contribute substantially to understanding the epidemiology of influenza, particularly for more severe disease.

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Dataset Information

Network analysis of global influenza spread.

Publications

Network analysis of global influenza spread.

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