Seroprevalence to Influenza A(H1N1) 2009 Virus—Where Are We?

Broberg, Eeva; Nicoll, A; Amato‐Gauci, Andrew J.

doi:10.1128/cvi.05072-11

Cited by 62 publications

(76 citation statements)

References 37 publications

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“…Most of the results discussed earlier correlate well with previous publications describing the dynamics of the pandemic virus infections in different countries and age distribution 1, 11, 18, 19, 20, 21, 22…”

Section: Discussionsupporting

confidence: 89%

“…Surprisingly, the baseline seroprevalence in age‐group 10–19 years was higher than for the older age‐groups (20–79). This has not been noticed in previous studies1 and was further investigated as described below.…”

Section: Resultsmentioning

confidence: 81%

“…Surveillance of clinical cases alone is limited in its ability to estimate the cumulative incidence of infection, especially when a large proportion of the infections are mild or asymptomatic, as was the case with the A(H1N1)pdm09 virus 1. Transmission, case‐fatality and hospitalization rates can only be evaluated when the true incidence of infection is assessed, based on sero‐epidemiological studies.…”

Section: Introductionmentioning

confidence: 99%

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The dynamics of infection and the persistence of immunity to A(H1N1)pdm09 virus in Israel

Weil

Shohat

Bromberg

et al. 2012

Influenza Resp Viruses

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BackgroundInfluenza virus A(H1N1)pdm09 first appeared in Israel in late April 2009, disappeared in mid‐March 2010, and reappeared in late October 2010. Symptoms were mostly mild without need for medical care.ObjectivesTo provide targets for future pandemic preparedness and response by evaluating the dynamics and cumulative incidence of A(H1N1)pdm09 infection, the virus‐specific seroprevalence (HI antibody titer >1:40) at the height of the pandemic, during its decline and thereafter.MethodsA cross‐sectional seroepidemiological study was conducted on 6911 serum samples collected before, during, and after the pandemic.ResultsCumulative incidence of infection derived from the differences between post‐ and pre‐pandemic seroprevalence was 54·1%, 32·9%, 22·9%, 14·8%, and 6·3% in age‐groups 0–9, 10–19, 20–49, 50–79, and ≥80 years, respectively, and 28·5% for all age‐groups combined. Vaccination could have contributed at the most 4·6% to the post‐pandemic population seroprevalence. High pre‐pandemic immune response (47·4%) found in a cohort aged 15–18 year was strongly associated with birth years 1990–1993. Morbidity began to decline in mid‐November 2009 at 32·8% population seroprevalence (45% in ages 0–19 year) and stopped in March 2010 at 43·4% population seroprevalence in February 2010 (70% in ages 0–19 year). Between February and September 2010, seroprevalence declined by 12·2% allowing virus recirculation from October 2010.ConclusionsOur study provides targets for controlling future influenza pandemics in Israel. Vaccination should focus on the younger age‐groups (0–19 year) which played a key role in transmission of the A(H1N1)pdm09 due to lack of background immunity (ages 0–9 year) and high exposure rates (ages 10–19 year).

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Section: Discussionsupporting

confidence: 89%

Section: Resultsmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

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The dynamics of infection and the persistence of immunity to A(H1N1)pdm09 virus in Israel

Weil

Shohat

Bromberg

et al. 2012

Influenza Resp Viruses

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“…We note that in practice epidemics rarely infect very high proportions of the population and this is largely due to heterogeneities in risk (see below) with some individuals with high risk contributing most to the value of R 0 whereas others with low risk more likely to escape infection. Hence, the final epidemic size should be viewed as an idealized calculation, although for the recent H1N1 (swine-flu) outbreak the value of R ∞ ≈ 0.37 (derived from R 0 ≈ 1.25 [18]) is in good qualitative agreement with finding from detailed serological studies [56].…”

Section: Long-term Behavioursupporting

confidence: 71%

Dynamics of infectious diseases

et al. 2014

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Modern infectious disease epidemiology has a strong history of using mathematics both for prediction and to gain a deeper understanding. However the study of infectious diseases is a highly interdisciplinary subject requiring insights from multiple disciplines, in particular a biological knowledge of the pathogen, a statistical description of the available data and a mathematical framework for prediction. Here we begin with the basic building blocks of infectious disease epidemiologythe SIS and SIR type models -before considering the progress that has been made over the recent decades and the challenges that lie ahead. Throughout we focus on the understanding that can be developed from relatively simple models, although accurate prediction will inevitably require far greater complexity beyond the scope of this review. In particular, we focus on three critical aspects of infectious disease models that we feel fundamentally shape their dynamics: heterogeneously structured populations; stochasticity; and spatial structure. Throughout we relate the mathematical models and their results to a variety of real-world problems.

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“…Many serological studies rely on residual sera from samples taken for clinical purposes or from blood banks, as they can be obtained quickly and often do not require informed consent. 2 However, these samples often have limited clinical or epidemiological data, including vaccine history.…”

Section: Fundingmentioning

confidence: 99%

Population-level susceptibility, severity and spread of pandemic influenza: design of, and initial results from, a pre-pandemic and hibernating pandemic phase study using cross-sectional data from the Health Survey for England (HSE)

Fragaszy

Quinlivan

Breuer³

et al. 2015

Public Health Res

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BackgroundAssessing severity and spread of a novel influenza strain at the start of a pandemic is critical for informing a targeted and proportional response. It requires community-level studies to estimate the burden of infection and disease. Rapidly initiating such studies in a pandemic is difficult. The study aims to establish an efficient system allowing real-time assessment of population susceptibility, spread of infection and clinical attack rates in the event of a pandemic.MethodsWe developed and appended additional survey questions and specimen collection to the Health Survey for England (HSE) – a large, annual, rolling nationally representative general population survey recruiting throughout the year – to enable rapid population-based surveys of influenza infection and disease during a pandemic. Using these surveys we can assess the spread of the virus geographically, by age and through time. The data generated can also provide denominators for national estimates of case fatality and hospitalisation rates.Phase 1: we compared retrospectively collected HSE illness rates during the first two infection waves of the 2009 pandemic with the Flu Watch study (a prospective community cohort). Monthly and seasonal age-specific rates of illness and proportion vaccinated were compared.Phase 2: we piloted blood specimen and data collection alongside the 2012–13 HSE. We are developing laboratory methods and protocols for real-time serological assays of a novel pandemic influenza virus using these specimens, and automated programmes for analysing and reporting illness and infection rates.Phase 3: during inter-pandemic years, the study enters a holding phase, where it is included in the yearly HSE ethics application and planning procedures, allowing rapid triggering in a pandemic.Phase 4: once retriggered, the study will utilise the methods developed in phase 2 to monitor the severity and spread of the pandemic in real time.ResultsPhase 1: the rates of reported illness during the first two waves in the HSE underestimated the community burden as measured by Flu Watch, but the patterns of illness by age and time were broadly comparable. The extent of underestimation was greatest for HSE participants interviewed later in the year compared with those interviewed closer to the pandemic. Vaccine uptake in the HSE study was comparable to independent national estimates and the Flu Watch study.Phases 2 and 3: illness data and serological samples from 2018 participants were collected in the 2012–13 HSE and transferred to the University College London Hospital. In the 2013 HSE and onwards, this project was included in the annual HSE ethics and planning rounds.ConclusionsThe HSE’s underestimation of illness rates during the first two waves of the pandemic is probably due to recall bias and the limitation of being able to report only one illness when multiple illnesses per season can occur. Changes to the illness questions (reporting only recent illnesses) should help minimise these issues. Additional prospective follow-up could improve measurement of disease incidence. The representative nature of the HSE allows accurate measurements of vaccine uptake.Study registrationThis study is registered as ISRCTN80214280.FundingThis project was funded by the NIHR Public Health Research programme and will be published in full inPublic Health Research; Vol. 3, No. 6. See the NIHR Journals Library website for further project information.

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Seroprevalence to Influenza A(H1N1) 2009 Virus—Where Are We?

Cited by 62 publications

References 37 publications

The dynamics of infection and the persistence of immunity to A(H1N1)pdm09 virus in Israel

The dynamics of infection and the persistence of immunity to A(H1N1)pdm09 virus in Israel

Dynamics of infectious diseases

Population-level susceptibility, severity and spread of pandemic influenza: design of, and initial results from, a pre-pandemic and hibernating pandemic phase study using cross-sectional data from the Health Survey for England (HSE)

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