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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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| 2. |
- Semeniuk, M., et al.
(author)
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Design and fabrication of low-cost renewable carbon electrode materials and their thermo-kinetics for sustainable energy storage applications
- 2022
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In: Materials Today Sustainability. - : Elsevier. - 2589-2347. ; 20
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Journal article (peer-reviewed)abstract
- To date, there have been few studies performed on the kinetics of thermal decomposition of renewable carbon and its use in energy storage devices. Novel renewable graphitic carbon from Acer saccharum, sugar maple (SM) heartwood, and Arachis hypogaea, peanut outer shell (PS), are used as anodes in lithium-ion coin cell batteries, with steady specific capacities of 180 mAh/g and 220 mAh/g, respectively, and both retain 100% columbic efficiency for over 350 cycles. Cyclic voltammetry reveals the different charge storage kinetic mechanisms of the two. The voltammogram of SM contains an oxidation peak indicating Li ion intercalation suitable for battery application, while PS lacks a peak; thus, showing potential for function as a supercapacitor. A kinetic study is undertaken on six carbon sources to improve the understanding of the thermal degradation process leading to renewable graphitic carbon. Consistent results are shown across the various model-free methods, indicating that they can accurately describe the devolatilization process. The renewable graphitic carbons morphological features were studied by SEM, XRD, Raman and nitrogen adsorption isotherms. As SM and PS compare favorably to the electrodes they replace, renewable graphitic carbon has the potential for use in a wide variety of novel applications, such as organic thin film transistors, fuel cells, organic batteries, supercapacitors, and other bioelectronics.
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