Forecasting the 2017/2018 seasonal influenza epidemic in England using multiple dynamic transmission models: A case study

Paul J. Birrell*, Xu-Sheng Zhang, Alice Corbella, Edwin Van Leeuwen, Nikolaos Panagiotopoulos, Katja Hoschler, Alex Elliot, Maryia McGee, Simon De Lusignan, Anne M. Presanis, Marc Baguelin, Maria Zambon, Andre Charlett, Richard Pebody, Daniela De Angelis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Background: Since the 2009 A/H1N1 pandemic, Public Health England have developed a suite of real-time statistical models utilising enhanced pandemic surveillance data to nowcast and forecast a future pandemic. Their ability to track seasonal influenza and predict heightened winter healthcare burden in the light of high activity in Australia in 2017 was untested. Methods: Four transmission models were used in forecasting the 2017/2018 seasonal influenza epidemic in England: a stratified primary care model using daily, region-specific, counts and virological swab positivity of influenza-like illness consultations in general practice (GP); a strain-specific (SS) model using weekly, national GP ILI and virological data; an intensive care model (ICU) using reports of ICU influenza admissions; and a synthesis model that included all data sources. For the first 12 weeks of 2018, each model was applied to the latest data to provide estimates of epidemic parameters and short-term influenza forecasts. The added value of pre-season population susceptibility data was explored. Results: The combined results provided valuable nowcasts of the state of the epidemic. Short-term predictions of burden on primary and secondary health services were initially highly variable before reaching consensus beyond the observed peaks in activity between weeks 3-4 of 2018. Estimates for R 0 were consistent over time for three of the four models until week 12 of 2018, and there was consistency in the estimation of R 0 across the SPC and SS models, and in the ICU attack rates estimated by the ICU and the synthesis model. Estimation and predictions varied according to the assumed levels of pre-season immunity. Conclusions: This exercise successfully applied a range of pandemic models to seasonal influenza. Forecasting early in the season remains challenging but represents a crucially important activity to inform planning. Improved knowledge of pre-existing levels of immunity would be valuable.

Original languageEnglish
Article number486
JournalBMC Public Health
Issue number1
Publication statusPublished - 15 Apr 2020

Bibliographical note

Funding Information:
This work was supported by the Medical Research Council (Unit programme number MC UU 00002/11), the National Institute for Health Research (NIHR) Health Protection Research Units (HPRU) in Respiratory Infections, both in partnership with Public Health England. Neither of the funding bodies had any role in setting up the case study, in the curation, interpretation or analysis of data, or in the writing of the manuscript.

Funding Information:
We acknowledge support from TPP and participating SystmOne practices and University of Nottingham, ClinRisk, EMIS and EMIS practices submitting data to the QSurveillance database and the PHE Real-time Syndromic Surveillance Team (GP in–hours). We also thank patients of the RCGP RSC practices who consented to having flu swabs taken and the RSC practices themselves for processing and sharing their data. The views expressed are those of the author(s) and not necessarily those of the MRC, PHE, or the NIHR.

Publisher Copyright:
© 2020 The Author(s).


  • Forecasting
  • GP consultations
  • Intensive care admissions
  • Nowcasting
  • Seasonal influenza
  • Transmission models


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