LSHTM modellers join forces with international researchers to predict European COVID-19 cases and deaths four weeks in advance
Europe is at a critical juncture in its fight against COVID-19. Many countries are currently relaxing restrictions, some against a backdrop of increasing numbers of cases and with new variants emerging, while still rolling out nationwide vaccination programmes. Evidence-based near-term forecasts of COVID-19 cases and deaths can therefore be of very helpful for public health decision-making.
This is the catalyst for the new European COVID-19 Forecast Hub, an initiative of the European Centre for Disease Prevention and Control (ECDC), with the support of the Centre for Mathematical Modelling of Infectious Diseases (CMMID) at the London School of Hygiene & Tropical Medicine.
Based on similar projects in the US, Germany and Poland, the hub collates weekly short-term forecasts from modelling teams around the world, predicting the number of COVID-19 cases and deaths four weeks ahead in 32 countries: 30 EU/EEA Member States, the UK and Switzerland. These are combined into an ‘ensemble’ and displayed alongside the individual forecasts.
Helen Johnson, Mathematical Modeller at ECDC, said: “We are excited about European modelling teams collaborating together with others from around the world to develop accurate and timely predictions of COVID-19 cases and deaths in Europe. We clearly foresee the potential impact on public health policy. This initiative yields insight now and also strengthens our capacity to respond to infectious disease threats in the future.”
During this pandemic, many forecasts have been produced, some very accurate, others less so. Scientists and their models need to remain responsive and flexible, especially given the increasing complexity of the pandemic, including changes in behaviour, “pandemic fatigue”, vaccine roll-out and newly-emerging variants. Some models account for these evolving factors less than others, leading to potentially biased forecasts. Collating forecasts into a single ‘ensemble’ forecast makes it possible to counteract the biases from different models, providing more accurate and reliable forecasts than a single model. This approach has been used successfully during outbreaks of other diseases, including Ebola, dengue, and influenza.
Sebastian Funk, Professor of Infectious Disease Dynamics at LSHTM, said: “By constructing an ‘ensemble’, we can leverage the contribution from each of the modelling teams, with the aim of creating the best possible forecast.”
Every week on Mondays, the teams submit their forecasts of cases and deaths, predicting up to four weeks ahead. The hub’s website displays current and past forecasts from each of the models, the ensemble forecast, and various evaluations of the submissions. The latest round of submissions, collated on 19 April 2021, contains 33 models representing a wide range of modelling methodologies.
According to Dr. Johannes Bracher, postdoctoral researcher in Statistics at Karlsruhe Institute of Technology, Germany: “Our experience from running a German and Polish COVID-19 Forecast Hub has been that this format provides a very useful feedback loop for modellers. It creates a track record of what models said at different points in time, allowing for apples-to-apples comparisons, systematic evaluation and, ultimately, improvement of models.”
However, the European Forecast Hub provides more than forecasts. It creates a platform for the participating modellers to meet colleagues from across Europe and elsewhere. A weekly online meeting is held, during which the teams share ideas and thoughts, representing a collaborative project that will be of benefit for years to come. During these meet-ups, the most recent forecasts are discussed and teams have the opportunity to present and discuss their forecast models or related work. There are currently 28 teams participating, representing eight different countries: Poland, Spain, Germany, Czechia, Italy, UK, Australia, and the USA. The Hub is also looking forward to welcoming further teams in the future.
This project was enabled by ECDC, supported by the Centre for Mathematical Modelling of Infectious Diseases (CMMID) at the London School of Hygiene & Tropical Medicine, and supported through collaboration with the University of Massachusetts-Amherst, the Karlsruhe Institute of Technology, and the Robert-Koch Institute.