Disease modelling has had considerable policy impact during the ongoing COVID-19 pandemic, and it is increasingly acknowledged that combining multiple models can improve the reliability of outputs. Here we report insights from ten weeks of collaborative short-term forecasting of COVID-19 in Germany and Poland (12 October–19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one- and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic.

Ultrafast magnetization dynamics in an epitaxially grown Co wedge-shaped layer sandwiched between Pt films has been studied by means of time-resolved magneto-optics. By changing either the external magnetic field or the thickness-dependent anisotropy field, we were able to tune the magnetization precession, as it depends on the effective magnetic field affecting the sample. A particularly interesting possibility of tuning occurs at the range of thicknesses near the spin reorientation transition (at d 0 = 2.3 nm), where the effective anisotropy field changes sign. To describe the measured dynamics we propose a model based on ultrafast thermal changes of the effective anisotropy field as a mechanism of triggering precession of the spins.

Regularly arranged magnetic out-of-plane patterns in continuous and flat films are promising for applications in data storage technology (bit patterned media) or transport of individual magnetic particles. Whereas topographic magnetic structures are fabricated by standard lithographical techniques, the fabrication of regularly arranged artificial domains in topographically flat films is difficult, since the free energy minimization determines the existence, shape, and regularity of domains. Here we show that keV He(+) ion bombardment of Au/Co/Au layer systems through a colloidal mask of hexagonally arranged spherical polystyrene beads enables magnetic patterning of regularly arranged cylindrical magnetic monodomains with out-of-plane magnetization embedded in a ferromagnetic matrix with easy-plane anisotropy. This colloidal domain lithography creates artificial domains via periodic lateral anisotropy variations induced by periodic defect density modulations. Magnetization reversal of the layer system observed by magnetic force microscopy shows individual disc switching indicating monodomain states.

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. Code and data availability All data and code are publicly available on Github: covid19-forecast-hub-europe/euro-hub-ensemble.