Transmission parameters of the A/H1N1 (2009) influenza virus pandemic: a review

Boëlle, Pierre‐Yves; Ansart, Séverine; Cori, Anne; Valleron, Alain‐Jacques

doi:10.1111/j.1750-2659.2011.00234.x

Cited by 142 publications

(140 citation statements)

References 59 publications

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“…For example, estimates for the (initial) reproductive number R init 0 , derived from the exponential growth rates, are centred on 1.8, with the region-specific estimates of the PR model being tightly distributed around this value. This is in broad agreement with other estimates for R 0 obtained from a review of 2009 pandemic transmission parameters, 50 and a slight increase on what had been estimated for the single region version of the model. 15 In a similar (single-region) modelling study, much higher estimates for the R 0 associated with the A/H1N1pdm virus have been derived, although this was over the course of a later third wave of pandemic infection occurring in the 51 Similarly, the estimates for the other transmission parameters are robust to the model specification [note the overlapping nature of the credible intervals (CrIs) in Table 4].…”

Section: Reconstructing the Epidemicsupporting

confidence: 91%

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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Section: Reconstructing the Epidemicsupporting

confidence: 91%

Real-time modelling of a pandemic influenza outbreak

Birrell

Pebody

Charlett

et al. 2017

Health Technol Assess

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“…The reproduction ratio without vaccination is equal to ρ(Bγ) = 2.1. The parameter values for both the generation interval and the reproduction ratio are in line with other studies in literature (Boëlle, Ansart, Cori, & Valleron, 2011;Vink, Bootsma, & Wallinga, 2014).…”

Section: Case Descriptionsupporting

confidence: 89%

The most efficient critical vaccination coverage and its equivalence with maximizing the herd effect

Duijzer

Jaarsveld

Wallinga

et al. 2016

Mathematical Biosciences

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Abstract'Critical vaccination coverages' are vaccination allocations that result in an effective reproduction ratio of one. In a population with interacting subpopulations there are many different critical vaccination coverages. To find the most efficient critical vaccination coverage, we define the following optimization problem: minimize the required amount of vaccines to obtain an effective reproduction ratio of exactly one. We prove that this optimization problem is equivalent to the problem of maximizing the proportion of susceptibles that escape infection during an epidemic (i.e., maximizing the herd effect).We propose an efficient general algorithm to solve these optimization problems based on PerronFrobenius theory. We study two special cases that provide further insight into these optimization problems. First, we derive an explicit analytic solution for the case of two interacting populations. Second, we derive an efficient algorithm for the case of multiple populations that interact according to separable mixing. In this algorithm the subpopulations are ordered by their ratio of population size to reproduction ratio. Allocating vaccines based on this priority order results in an optimal allocation. We apply our solutions in a case study for pre-pandemic vaccination in the initial phase of an influenza pandemic where the entire population is susceptible to the new influenza virus. The results show that for the optimal allocation the critical vaccination coverage is achieved for a much smaller amount of vaccines as compared to allocations proposed previously.

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“…Third, the duration of follow-up in our study was from 7-12 days after symptom onset in index case patients; hence, some information on long durations of virus shedding for secondary and tertiary cases may have been missed. However, with an average serial interval of about 3 days [41], the effect of such censoring should be minimal. Fourth, nose and throat swab specimens were pooled in our studies, and while we might expect a high correlation in viral loads in the 2 sites in future iterations of the work, it will be interesting to test a range of sites (including viral load in nose, throat, and exhaled breath) to determine which sites provide the best proxy measure of infectivity.…”

Section: Discussionmentioning

confidence: 99%

Influenza A Virus Shedding and Infectivity in Households

Tsang¹,

Cowling²,

Fang³

et al. 2015

J Infect Dis.

109

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Background. Viral shedding is often considered to correlate with the infectivity of influenza, but the evidence for this is limited.Methods. In a detailed study of influenza virus transmission within households in 2008-2012, index case patients with confirmed influenza were identified in outpatient clinics, and we collected nose and throat swab specimens for testing by reverse-transcription polymerase chain reaction from all household members regardless of illness. We used individual-based hazard models to characterize the relationship between viral load (V ) and infectivity.Results. Assuming that infectivity was proportional to viral load V gave the worst fit, because it strongly overestimated the proportion of transmission occurring at symptom onset. Alternative models assuming that infectivity was proportional to a various functions of V provided better fits, although they all overestimated the proportion of transmission occurring >3 days after symptom onset. The best fitting model assumed that infectivity was proportion to V γ , with estimates of γ = 0.136 and γ = 0.156 for seasonal influenza A(H1N1) and A(H3N2) respectively.Conclusions. All the models we considered that used viral loads to approximate infectivity of a case imperfectly explained the timing of influenza secondary infections in households. Identification of more accurate correlates of infectivity will be important to inform control policies and disease modeling.

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Transmission parameters of the A/H1N1 (2009) influenza virus pandemic: a review

Cited by 142 publications

References 59 publications

Real-time modelling of a pandemic influenza outbreak

Real-time modelling of a pandemic influenza outbreak

The most efficient critical vaccination coverage and its equivalence with maximizing the herd effect

Influenza A Virus Shedding and Infectivity in Households

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