The European Covid-19 Forecast Hub is run in collaboration between the Epiforecasts team, which is part of the Centre for Mathematical Modelling of Infectious Diseases at the London School of Hygiene & Tropical Medicine, and the European Centre for Disease Control and Prevention (ECDC).
The Hub collates and combines short-term forecasts of Covid-19 across Europe (EU and EFTA countries and the UK) generated by different independent modelling teams using a wide range of approaches. The underlying approach was pioneered by the Reich lab and follows similar projects in the USA and in Germany and Poland. Another related project is the US scenario hub.
For more information on technical details and contributors, visit the project pages on github. Teams from anywhere in the world are invited to submit forecasts once a week for one or more of the countries. Take a look at the submission instructions and get in touch with any questions. Please note that the participating teams do not represent ECDC or the London School of Hygiene & Tropcial Medicine.
The main aim of the European Covid-19 Forecast Hub is to provide decision makers and the general public with reliable information about the near-term future trajectory of the pandemic. This is achieved by collating forecasts from different models into an ensemble, an approach that has in the past proven to provide consistently better performance than any individual modelling approach.
Secondary aims are to gain insight into the predictive performance of different modelling approaches, to assess the quality of forecasts with respect to different measures of disease severity (e.g., cases or deaths), and to maintain a community of infectious disease modellers underpinned by an open-science ethos.
The European Covid-19 Forecast Hub aims at providing forecasts in real time and evaluating systematically how reliable these forecasts are. Please keep the following in mind when interpreting forecasts:
We constantly monitor the performance of forecasts. In the evaluation section we provide, among other measures, the empirical coverage proportions of forecast intervals for different targets and forecast horizons. This makes it possible to assess whether past prediction intervals of the ensemble have been reliable for a given target.