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: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.

Project description:BackgroundThe effectiveness of currently licensed vaccines against influenza has not been clearly established, especially among individuals at increased risk for complications from influenza. We used a test-negative approach to estimate influenza vaccine effectiveness (IVE) against hospitalization with laboratory-confirmed influenza based on data collected from the Global Influenza Hospital Surveillance Network (GIHSN).Methods and findingsThis was a multi-center, prospective, active surveillance, hospital-based epidemiological study during the 2012-2013 influenza season. Data were collected from hospitals participating in the GIHSN, including five in Spain, five in France, and four in the Russian Federation. Influenza was confirmed by reverse transcription-polymerase chain reaction. IVE against hospitalization for laboratory-confirmed influenza was estimated for adult patients targeted for vaccination and who were swabbed within 7 days of symptom onset. The overall adjusted IVE was 33% (95% confidence interval [CI], 11% to 49%). Point estimates of IVE were 23% (95% CI, -26% to 53%) for influenza A(H1N1)pdm09, 30% (95% CI, -37% to 64%) for influenza A(H3N2), and 43% (95% CI, 17% to 60%) for influenza B/Yamagata. IVE estimates were similar in subjects <65 and ≥65 years of age (35% [95% CI, -15% to 63%] vs.31% [95% CI, 4% to 51%]). Heterogeneity in site-specific IVE estimates was high (I2 = 63.4%) for A(H1N1)pdm09 in patients ≥65 years of age. IVE estimates for influenza B/Yamagata were homogenous (I2 = 0.0%).ConclusionsThese results, which were based on data collected from the GIHSN during the 2012-2013 influenza season, showed that influenza vaccines provided low to moderate protection against hospital admission with laboratory-confirmed influenza in adults targeted for influenza vaccination. In this population, IVE estimates against A(H1N1)pdm09 were sensitive to age group and study site. Influenza vaccination was moderately effective in preventing admissions with influenza B/Yamagata for all sites and age groups.

Dataset Information

Network analysis of global influenza spread.

Publications

Network analysis of global influenza spread.

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