Seroepidemiologic investigation of an outbreak of pandemic influenza <scp>A</scp> H1N1 2009 aboard a <scp>US N</scp>avy <scp>V</scp>essel—<scp>S</scp>an <scp>D</scp>iego, 2009

Khaokham, Christina B.; Selent, Monica U.; Loustalot, Fleetwood; Zarecki, Shauna Mettee; Harrington, Douglas S.; Hoke, Eileen; Faix, Dennis J.; Ortiguerra, Ryan G.; Alvarez, Bryan; Almond, Nathaniel; McMullen, Kellie; Cadwell, Betsy L.; Uyeki, Timothy M.; Blair, Patrick J.; Waterman, Stephen H.

doi:10.1111/irv.12100

Cited by 20 publications

(21 citation statements)

References 18 publications

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“…The proportion of A(H1N1)pdm09 infections in our cohort that were subclinical was very high (83.3%). This proportion was higher than some prior estimates (approximately 50%) from a few comparable studies [11]–[13]. This may be due to differences in our study design.…”

Section: Discussioncontrasting

confidence: 77%

High Rate of A(H1N1)pdm09 Infections among Rural Thai Villagers, 2009–2010

et al. 2014

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BackgroundPandemic influenza A(H1N1)pdm09 emerged in Thailand in 2009. A prospective longitudinal adult cohort and household transmission study of influenza-like illness (ILI) was ongoing in rural Thailand at the time of emergence. Symptomatic and subclinical A(H1N1)pdm09 infection rates in the cohort and among household members were evaluated.MethodsA cohort of 800 Thai adults underwent active community-based surveillance for ILI from 2008–2010. Acute respiratory samples from ILI episodes were tested for A(H1N1)pdm09 by qRT-PCR; acute and 60-day convalescent blood samples were tested by A(H1N1)pdm09 hemagglutination inhibition assay (HI). Enrollment, 12-month and 24-month follow-up blood samples were tested for A(H1N1)pdm09 seroconversion by HI. Household members of influenza A-infected cohort subjects with ILI were enrolled in household transmission investigations in which day 0 and 60 blood samples and acute respiratory samples were tested by either qRT-PCR or HI for A(H1N1)pdm09. Seroconversion between annual blood samples without A(H1N1)pdm09-positive ILI was considered as subclinical infection.ResultsThe 2-yr cumulative incidence of A(H1N1)pdm09 infection in the cohort in 2009/2010 was 10.8% (84/781) with an annual incidence of 1.2% in 2009 and 9.7% in 2010; 83.3% of infections were subclinical (50% in 2009 and 85.9% in 2010). The 2-yr cumulative incidence was lowest (5%) in adults born ≤1957. The A(H1N1)pdm09 secondary attack rate among household contacts was 47.2% (17/36); 47.1% of these infections were subclinical. The highest A(H1N1)pdm09 secondary attack rate among household contacts (70.6%, 12/17) occurred among children born between 1990 and 2003.ConclusionSubclinical A(H1N1)pdm09 infections in Thai adults occurred frequently and accounted for a greater proportion of all A(H1N1)pdm09 infections than previously estimated. The role of subclinical infections in A(H1N1)pdm09 transmission has important implications in formulating strategies to predict and prevent the spread of A(H1N1)pdm09 and other influenza virus strains.

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Section: Discussioncontrasting

confidence: 77%

High Rate of A(H1N1)pdm09 Infections among Rural Thai Villagers, 2009–2010

et al. 2014

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“…During the 2009 pandemic, attack rates among military personnel measured during clusters of infection among specific military units ranged from as low as 3% [7] and as high as 70% [8]. In confined settings, such as aboard navy ships, serologic attack rates of influenza among US and foreign military crew have been estimated to be between 30–50% [9, 10]. Estimates of the proportion of the US military infected with influenza, risk factors for severe outcomes among those infected with influenza, and trends in incidence and cross-protective immunity are important for understanding the burden of influenza in this unique cohort.…”

Section: Introductionmentioning

confidence: 99%

Risk factors for severe outcomes among members of the United States military hospitalized with pneumonia and influenza, 2000–2012

Kerkhove

Cooper

Cost

et al. 2015

Vaccine

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Background The progression from hospitalization for a respiratory infection to requiring substantial supportive therapy is a key stage of the influenza severity pyramid. Respiratory infections are responsible for 300,000 to 400,000 medical encounters each year among US military personnel, some of which progress to severe acute respiratory infections. Methods We obtained data on 11,086 hospitalizations for pneumonia and influenza (P&I) among non-recruit US military service members during the period of 1 January 2000 through 31 December 2012. From these, we identified 512 P&I hospitalizations that progressed to severe episodes using standard case definitions. We evaluated the effect of demographic and occupational characteristics, comorbid conditions, and history of influenza vaccination on the risk of a hospitalized P&I case becoming a severe case. We also evaluated the risk of a severe outcome and the length of time since influenza vaccination (within 180, 60 and 30 days). Results The median age of subjects at the time of the P&I episode was 32 years (range, 28–40) and subjects were predominantly male (89.5%). In a univariate analysis, demographic risk factors for a severe episode included service in the US Air Force (RR=1.6 relative to US Army, 95%CI 1.3-2.1), US Coast Guard (RR=2.1, 1.2-3.7) or US Navy (RR=1.4, 1.1-1.8). Being born in the US and recent influenza vaccination (within 180 days of episode) were protective against developing severe disease. Among comorbid conditions, univariate risk factors for severe disease included chronic renal or liver disease (RR=4.98, 95%CI 4.1-6.1), diseases of the circulatory system (RR=3.1, 95%CI 2.6-3.7), diabetes mellitus (RR=2.3, 95%CI 1.5-3.6), obesity (RR=1.6, 95%CI 1.2-2.1), cancer (RR=1.6, 95%CI 1.3-2.0) and chronic obstructive pulmonary disease (RR=1.4, 95%CI 1.1-1.7). Although many of the risk factors found to be significant in univariate analysis were no longer significant under a multivariate analysis, receipt of any influenza vaccine within 180 days of episode remained protective (RR=0.81, 95%CI 0.67-0.99), while serving in the US Coast Guard (RR=1.9, 95%CI 1.1-3.4) or US Air Force (RR=1. 5, 95%CI 1.2-2.0), presence of renal or liver disease (RR=3.6, 95%CI 2.9-4.6) and diseases of the circulatory system (RR=2.2, 95%CI 1.8-2.8), remained significantly associated with a higher risk of developing severe disease. Conclusions In a large cohort, after adjusting for many possible risk factors, influenza vaccination was protective against severe episodes among P&I hospitalizations. The service-specific (US Coast Guard or US Air Force) increased risk may represent some differences in data (e.g., coding or reporting practices) as opposed to genuine differences in physiological outcome. Our findings suggest that renal and liver disease as well as diseases of the circulatory system may contribute to influenza severity in this population independently of age and other potential comorbidities. These findings provide additional evidence for the prioritization of speci...

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“…Estimates for the proportion symptomatic, ϕ, do appear to be rather low, although consistent across approaches and with an estimate of 11% based on a closely observed outbreak. 53 Comparison between meta-region and parallel-region modelling…”

Section: Reconstructing the Epidemicmentioning

confidence: 99%

Real-time modelling of a pandemic influenza outbreak

Birrell

Pebody

Charlett

et al. 2017

Health Technol Assess

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Background Real-time modelling is an essential component of the public health response to an outbreak of pandemic influenza in the UK. A model for epidemic reconstruction based on realistic epidemic surveillance data has been developed, but this model needs enhancing to provide spatially disaggregated epidemic estimates while ensuring that real-time implementation is feasible. Objectives To advance state-of-the-art real-time pandemic modelling by (1) developing an existing epidemic model to capture spatial variation in transmission, (2) devising efficient computational algorithms for the provision of timely statistical analysis and (3) incorporating the above into freely available software. Methods Markov chain Monte Carlo (MCMC) sampling was used to derive Bayesian statistical inference using 2009 pandemic data from two candidate modelling approaches: (1) a parallel-region (PR) approach, splitting the pandemic into non-interacting epidemics occurring in spatially disjoint regions; and (2) a meta-region (MR) approach, treating the country as a single meta-population with long-range contact rates informed by census data on commuting. Model discrimination is performed through posterior mean deviance statistics alongside more practical considerations. In a real-time context, the use of sequential Monte Carlo (SMC) algorithms to carry out real-time analyses is investigated as an alternative to MCMC using simulated data designed to sternly test both algorithms. SMC-derived analyses are compared with ‘gold-standard’ MCMC-derived inferences in terms of estimation quality and computational burden. Results The PR approach provides a better and more timely fit to the epidemic data. Estimates of pandemic quantities of interest are consistent across approaches and, in the PR approach, across regions (e.g. R 0 is consistently estimated to be 1.76–1.80, dropping by 43–50% during an over-summer school holiday). A SMC approach was developed, which required some tailoring to tackle a sudden ‘shock’ in the data resulting from a pandemic intervention. This semi-automated SMC algorithm outperforms MCMC, in terms of both precision of estimates and their timely provision. Software implementing all findings has been developed and installed within Public Health England (PHE), with key staff trained in its use. Limitations The PR model lacks the predictive power to forecast the spread of infection in the early stages of a pandemic, whereas the MR model may be limited by its dependence on commuting data to describe transmission routes. As demand for resources increases in a severe pandemic, data from general practices and on hospitalisations may become unreliable or biased. The SMC algorithm developed is semi-automated; therefore, some statistical literacy is required to achieve optimal performance. Conclusions Following the objectives, this study found that timely, spatially disaggregate, real-time pandemic inference is feasible, and a system that assumes data as per pandemic preparedness plans has been developed for rapid implementation. Future work recommendations Modelling studies investigating the impact of pandemic interventions (e.g. vaccination and school closure); the utility of alternative data sources (e.g. internet searches) to augment traditional surveillance; and the correct handling of test sensitivity and specificity in serological data, propagating this uncertainty into the real-time modelling. Trial registration Current Controlled Trials ISRCTN40334843. Funding This project was funded by the National Institute for Health Research (NIHR) Health Technology programme and will be published in full in Health Technology Assessment; Vol. 21, No. 58. See the NIHR Journals Library website for further project information. Daniela De Angelis was supported by the UK Medical Research Council (Unit Programme Number U105260566) and by PHE. She received funding under the NIHR grant for 10% of her time. The rest of her salary was provided by the MRC and PHE jointly.

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Seroepidemiologic investigation of an outbreak of pandemic influenza A H1N1 2009 aboard a US Navy Vessel—San Diego, 2009

Cited by 20 publications

References 18 publications

High Rate of A(H1N1)pdm09 Infections among Rural Thai Villagers, 2009–2010

High Rate of A(H1N1)pdm09 Infections among Rural Thai Villagers, 2009–2010

Risk factors for severe outcomes among members of the United States military hospitalized with pneumonia and influenza, 2000–2012

Real-time modelling of a pandemic influenza outbreak

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