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Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations (preprint)
Katharine Sherratt; Hugo Gruson; Rok Grah; Helen Johnson; Rene Niehus; Bastian Prasse; Frank Sandman; Jannik Deuschel; Daniel Wolffram; Sam Abbott; Alexander Ullrich; Graham Gibson; Evan L Ray; Nicholas G Reich; Daniel Sheldon; Yijin Wang; Nutcha Wattanachit; Lijing Wang; Jan Trnka; Guillaume Obozinski; Tao Sun; Dorina Thanou; Loic Pottier; Ekaterina Krymova; Maria Vittoria Barbarossa; Neele Leithauser; Jan Mohring; Johanna Schneider; Jaroslaw Wlazlo; Jan Fuhrmann; Berit Lange; Isti Rodiah; Prasith Baccam; Heidi Gurung; Steven Stage; Bradley Suchoski; Jozef Budzinski; Robert Walraven; Inmaculada Villanueva; Vit Tucek; Martin Smid; Milan Zajicek; Cesar Perez Alvarez; Borja Reina; Nikos I Bosse; Sophie Meakin; Pierfrancesco Alaimo Di Loro; Antonello Maruotti; Veronika Eclerova; Andrea Kraus; David Kraus; Lenka Pribylova; Bertsimas Dimitris; Michael Lingzhi Li; Soni Saksham; Jonas Dehning; Sebastian Mohr; Viola Priesemann; Grzegorz Redlarski; Benjamin Bejar; Giovanni Ardenghi; Nicola Parolini; Giovanni Ziarelli; Wolfgang Bock; Stefan Heyder; Thomas Hotz; David E. Singh; Miguel Guzman-Merino; Jose L Aznarte; David Morina; Sergio Alonso; Enric Alvarez; Daniel Lopez; Clara Prats; Jan Pablo Burgard; Arne Rodloff; Tom Zimmermann; Alexander Kuhlmann; Janez Zibert; Fulvia Pennoni; Fabio Divino; Marti Catala; Gianfranco Lovison; Paolo Giudici; Barbara Tarantino; Francesco Bartolucci; Giovanna Jona Lasinio; Marco Mingione; Alessio Farcomeni; Ajitesh Srivastava; Pablo Montero-Manso; Aniruddha Adiga; Benjamin Hurt; Bryan Lewis; Madhav Marathe; Przemyslaw Porebski; Srinivasan Venkatramanan; Rafal Bartczuk; Filip Dreger; Anna Gambin; Krzysztof Gogolewski; Magdalena Gruziel-Slomka; Bartosz Krupa; Antoni Moszynski; Karol Niedzielewski; Jedrzej Nowosielski; Maciej Radwan; Franciszek Rakowski; Marcin Semeniuk; Ewa Szczurek; Jakub Zielinski; Jan Kisielewski; Barbara Pabjan; Kirsten Holger; Yuri Kheifetz; Markus Scholz; Marcin Bodych; Maciej Filinski; Radoslaw Idzikowski; Tyll Krueger; Tomasz Ozanski; Johannes Bracher; Sebastian Funk.
medrxiv; 2022.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2022.06.16.22276024
ABSTRACT

Background:

Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022.

Methods:

We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported from a standardised source over the next one to four weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models past predictive performance.

Results:

Over 52 weeks we collected and combined up to 28 forecast models for 32 countries. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 84% of participating models forecasts of incident cases (with a total N=862), and 92% of participating models forecasts of deaths (N=746). Across a one to four week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over four weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models.

Conclusions:

Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than two weeks.
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Full text: Available Collection: Preprints Database: medRxiv Main subject: Communicable Diseases / Death / COVID-19 Language: English Year: 2022 Document Type: Preprint

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Full text: Available Collection: Preprints Database: medRxiv Main subject: Communicable Diseases / Death / COVID-19 Language: English Year: 2022 Document Type: Preprint