Evaluation of respiratory disease hospitalisation forecasts using synthetic outbreak data

Forecasts of hospitalisations of infectious diseases play an important role for allocating healthcare resources during epidemics and pandemics. Large-scale analysis of model forecasts during the COVID-19 pandemic has shown that the model rank distribution with respect to accuracy is heterogeneous and that ensemble forecasts have the highest average accuracy.

Building on that work we generated a maximally diverse synthetic dataset of 324 different hospitalisation time-series that correspond to different disease characteristics and public health responses. We evaluated forecasts from 14 component models and 6 different ensembles.

Our results show that component model accuracy was heterogeneous and varied depending on the current rate of disease transmission. Going from 7 day to 14 day forecasts mechanistic models improved in relative accuracy compared to statistical models. A novel adaptive ensemble method outperforms all other ensembles on synthetic data, and is closely followed by a median ensemble. When evaluated on data from the COVID-19 pandemic, component models performed worse, but the ensemble accuracy was still high, with the median ensemble performing best. We also investigated the relationship between ensemble error and variability of component forecasts and show that the coefficient of variation is predictive of future error.

Our findings have the potential to improve epidemic forecasting, in particular the adaptive ensemble and the ability to assign confidence to ensemble forecasts at the time of prediction based on component forecast variability.