  Scientific advice to the UK government throughout the COVID-19 pandemic has
been informed by ensembles of epidemiological models provided by members of the
Scientific Pandemic Influenza group on Modelling (SPI-M). Among other
applications, the model ensembles have been used to forecast daily incidence,
deaths and hospitalizations. The models differ in approach (e.g. deterministic
or agent-based) and in assumptions made about the disease and population. These
differences capture genuine uncertainty in the understanding of disease
dynamics and in the choice of simplifying assumptions underpinning the model.
Although analyses of multi-model ensembles can be logistically challenging when
time-frames are short, accounting for structural uncertainty can improve
accuracy and reduce the risk of over-confidence in predictions. In this study,
we compare the performance of various ensemble methods to combine short-term
(14 day) COVID-19 forecasts within the context of the pandemic response. We
address practical issues around the availability of model predictions and make
some initial proposals to address the short-comings of standard methods in this
challenging situation.
