Forecasting the 2017/2018 seasonal influenza epidemic in England using multiple dynamic transmission models: a case study - Birrell, PJ; Zhang, X-S; Corbella, A; van Leeuwen, E; Panagiotopoulos, N; Hoschler, K; Elliot, AJ; McGee, M; de Lusignan, S; Presanis, AM; Baguelin, M; Zambon, M; et al. (BioMed Central, 2020)
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.