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- Sherratt, K., et al.
(author)
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Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations
- 2023
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In: eLIFE. - : eLife Sciences Publications Ltd. - 2050-084X. ; 12
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Journal article (peer-reviewed)abstract
- 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 by a standardised source for 32 countries over the next 1–4 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 forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. 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 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 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 2 weeks.
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- Kalvius, G. M., et al.
(author)
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Study of magnetic clusters in granular CoxCu1- x
- 2008
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In: Physica Scripta. - : IOP Publishing. - 0031-8949 .- 1402-4896. ; 77:2, s. 025705-
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Journal article (peer-reviewed)abstract
- Samples of CoxCu1-x (x = 0.05, 0.02 and 0.01) produced by melt spinning and subsequent annealing were studied by muon spin rotation/relaxation (mu SR) spectroscopy with the aim to gain information on the dependence of the local magnetic properties like the magnitude, the distribution and the temporal behavior of the magnetic field at the muon site as function of Co concentration. In addition, we investigated for Co0.02Cu0.98 the changes in mu SR response as function of annealing times and temperatures. In all alloys, a spin-glass-like magnetic cluster state was seen well below the susceptibility cusp temperature. Above this temperature, cluster spin dynamics sets in but with low fluctuation rates even at high temperatures reflecting the presence of strong cluster - cluster couplings. A free paramagnetic cluster state is not formed. Spin dynamic properties are different for the x = 0(05 and 0.02 compounds and the x = 0(01 alloy. In the former compounds, the coexistence of a slower and a faster fluctuating fraction is found, arising from a non-uniform cluster size distribution or a non-random spatial distribution of clusters. In the latter compound, the spin dynamical state is homogeneous, but shows an unusual mu SR response requiring the presence of simultaneous fluctuations in spin orientation and moment magnitude. It further points to the presence of a partial loss of magnetic clusters. The variation of annealing times and temperatures for Co0.02Cu0.98 influences details of mu SR spectral parameters but leaves the overall response the same.
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- Natalino, Carlos, et al.
(author)
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Optimal lifetime-aware operation of green optical backbone networks
- 2016
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In: IEEE Journal on Selected Areas in Communications. - : Institute of Electrical and Electronics Engineers (IEEE). - 0733-8716 .- 1558-0008. ; 34:12, s. 3915-3926
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Journal article (peer-reviewed)abstract
- This paper targets the lifetime-aware management of a set of optical line amplifiers (OLAs) in an optical network exploiting sleep mode (SM) in order to save energy. We first present a simple model to predict the OLA lifetime. We then provide different mixed-integer linear programming (MILP) formulations, which jointly consider energy saving and lifetime. The proposed MILP formulations are then solved on different realistic scenarios, by taking into account the spatial and temporal variations of traffic demands. Results show that our lifetime-aware approach outperforms classical energy saving ILP formulations, which instead tend to notably decrease the OLA lifetime. More important, the proposed approaches can achieve a good lifetime performance without consuming significantly more energy than purely energy-aware strategies.
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