Tracking and forecasting seasonal influenza, 2017-8: A in-depth modelling challenge
PHE ePoster Library. Birrell P. Apr 10, 2019; 257530; 15461
Paul Birrell
Paul Birrell
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Abstract Background:
Since the 2009 A/H1N1 pandemic, a suite of statistical models have been designed to use PHE's routinely collected surveillance data streams to monitor pandemic evolution. These models can also be used to track seasonal influenza activity to estimate and predict healthcare burden, a key component of any effort to mitigate heightened seasonal demand on services.
: Four models, each based on classic SIR/SEIR transmission dynamics were used: a stratified primary care model using daily counts of GP consultations and virological swab positivity, all stratified by region; a strain-specific model using weekly GP consultation and virological data; an intensive care model using reports of ICU admissions; and a hybrid model that jointly accounted for all data. Over the first 12 weeks of 2018 each model was applied to the latest available data to provide estimates of key epidemic parameters and forecasts of the coming influenza activity..
: Estimation of epidemic features and parameters was generally highly variable prior to the observed peak in activity, around 2018w3-4. Beyond this time, parameter estimates remained stable, while predictions of peak weeks in activity gradually drifted later in the season due to a slower decline in the post-peak data than was anticipated.
Each model uses distinct datasets and therefore cannot be directly compared. However, combined, they can provide an extremely valuable tool to produce 'nowcasts' of the overall burden on services and predict the burden over the coming weeks. Estimates of reproductive numbers were consistent over time and across methods, though models could not accurately forecast beyond a week ahead with any great predictive power. Improved knowledge of pre-existing levels of immunity would increase the ability to foresee both the timing and the scale of epidemic peaks. Funding This work was supported by the MRC (Unit programme number MC UU 00002/11) and PHE.
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