QFLU: new influenza monitoring in UK primary care to support pandemic influenza planning

Hippisley‐Cox, Julia; Smith, Stanley K.; Smith, Gillian; Porter, Andrea L.; Heaps, Mike; Holland, Ryan; Fenty, Justin; Harcourt, Sally; George, Rinu J.; Charlett, André; Pebody, Richard; Painter, Mark M.

doi:10.2807/esw.11.25.02980-en

Cited by 6 publications

(8 citation statements)

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“…By the end of June 2009, the most reliable estimates of epidemic growth rate for the H1N1pdm pandemic were those obtained from the comprehensive (>25% population coverage) Qsurveillance sentinel surveillance system for influenza-like-illness operating in England [28] . Fitting an exponential model with non-zero intercept to data from the Qsurveillance network data available to July 1 [29] the estimated real-time exponential growth rate is 0.141/day (95% CI: 0.127–0.156), corresponding to a doubling time of 4.9 days (95% CI: 4.4–5.5 days).…”

Section: Methodsmentioning

confidence: 99%

Determinants of the Spatiotemporal Dynamics of the 2009 H1N1 Pandemic in Europe: Implications for Real-Time Modelling

et al. 2011

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Influenza pandemics in the last century were characterized by successive waves and differences in impact and timing between different regions, for reasons not clearly understood. The 2009 H1N1 pandemic showed rapid global spread, but with substantial heterogeneity in timing within each hemisphere. Even within Europe substantial variation was observed, with the UK being unique in experiencing a major first wave of transmission in early summer and all other countries having a single major epidemic in the autumn/winter, with a West to East pattern of spread. Here we show that a microsimulation model, parameterised using data about H1N1pdm collected by the beginning of June 2009, explains the occurrence of two waves in UK and a single wave in the rest of Europe as a consequence of timing of H1N1pdm spread, fluxes of travels from US and Mexico, and timing of school vacations. The model provides a description of pandemic spread through Europe, depending on intra-European mobility patterns and socio-demographic structure of the European populations, which is in broad agreement with observed timing of the pandemic in different countries. Attack rates are predicted to depend on the socio-demographic structure, with age dependent attack rates broadly agreeing with available serological data. Results suggest that the observed heterogeneity can be partly explained by the between country differences in Europe: marked differences in school calendars, mobility patterns and sociodemographic structures. Moreover, higher susceptibility of children to infection played a key role in determining the epidemiology of the 2009 pandemic. Our work shows that it would have been possible to obtain a broad-brush prediction of timing of the European pandemic well before the autumn of 2009, much more difficult to achieve with simpler models or pre-pandemic parameterisation. This supports the use of models accounting for the structure of complex modern societies for giving insight to policy makers.

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Section: Methodsmentioning

confidence: 99%

Determinants of the Spatiotemporal Dynamics of the 2009 H1N1 Pandemic in Europe: Implications for Real-Time Modelling

et al. 2011

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“…The influenza syndromic surveillance capability of the Health Protection Agency (HPA) includes the Royal College of General Practitioners (RCGP) Weekly Returns Service 5 and the QSurveillance system (University of Nottingham and Egton Medical Information Systems Ltd), which analyse anonymised morbidity data automatically extracted from networks of participating general practices. 6 Since the beginning of the influenza A/H1N1 2009 epidemic, general practice diagnoses of influenza-like illness and a range of other respiratory tract infections (rates per 100 000 population) have been monitored on a daily (QSurveillance) and weekly basis (QSurveillance, RCGP), providing information for regular situation reports. 7 Throughout May and most of June 2009, however, general practice consultation rates for influenza-like illness remained well below the upper limit for "baseline activity" of 30 cases per 100 000 population.…”

Section: Introductionmentioning

confidence: 99%

Monitoring the emergence of community transmission of influenza A/H1N1 2009 in England: a cross sectional opportunistic survey of self sampled telephone callers to NHS Direct

Elliot¹,

Powers

Thornton

et al. 2009

BMJ

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Objective To evaluate ascertainment of the onset of community transmission of influenza A/H1N1 2009 (swine flu) in England during the earliest phase of the epidemic through comparing data from two surveillance systems.Design Cross sectional opportunistic survey.Study samples Results from self samples by consenting patients who had called the NHS Direct telephone health line with cold or flu symptoms, or both, and results from Health Protection Agency (HPA) regional microbiology laboratories on patients tested according to the clinical algorithm for the management of suspected cases of swine flu.Setting Six regions of England between 24 May and 30 June 2009. Main outcome measure Proportion of specimens with laboratory evidence of influenza A/H1N1 2009.Results Influenza A/H1N1 2009 infections were detected in 91 (7%) of the 1385 self sampled specimens tested. In addition, eight instances of influenza A/H3 infection and two cases of influenza B infection were detected. The weekly rate of change in the proportions of infected individuals according to self obtained samples closely matched the rate of increase in the proportions of infected people reported by HPA regional laboratories. Comparing the data from both systems showed that local community transmission was occurring in London and the West Midlands once HPA regional laboratories began detecting 100 or more influenza A/H1N1 2009 infections, or a proportion positive of over 20% of those tested, each week.Conclusions Trends in the proportion of patients with influenza A/H1N1 2009 across regions detected through clinical management were mirrored by the proportion of NHS Direct callers with laboratory confirmed infection. The initial concern that information from HPA regional laboratory reports would be too limited because it was based on testing patients with either travel associated risk or who were contacts of other influenza cases was unfounded. Reports from HPA regional laboratories could be used to recognise the extent to which local community transmission was occurring.

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“…17 The primary difference is in the data used. Instead of using counts of case confirmations alone, an array of different data sets were combined: age-and region-stratified data on GP consultations for ILI; 18 virological positivity data from individuals reporting symptoms of ILI available through the Royal College of General Practitioners (RCGP) surveillance network and the Regional Microbiology Network (RMN) of the HPA; virological case confirmations from the early part of the epidemic; and data on the seropositivity of sera samples taken before and during the 2009 pandemic and held by the Weekly Returns Service of the RCGP. The modelling details of this second approach are detailed in Modelling methodology: single region model and Appendix 1 for more details.…”

Section: Discussionmentioning

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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QFLU: new influenza monitoring in UK primary care to support pandemic influenza planning

Abstract: In 2006, a new national influenza surveillance system began reporting in the United Kingdom. QFLU is a new daily data collection

Cited by 6 publications

References 0 publications

Determinants of the Spatiotemporal Dynamics of the 2009 H1N1 Pandemic in Europe: Implications for Real-Time Modelling

Determinants of the Spatiotemporal Dynamics of the 2009 H1N1 Pandemic in Europe: Implications for Real-Time Modelling

Monitoring the emergence of community transmission of influenza A/H1N1 2009 in England: a cross sectional opportunistic survey of self sampled telephone callers to NHS Direct

Real-time modelling of a pandemic influenza outbreak

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