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- Sherratt, K., et al.
(författare)
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Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations
- 2023
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Ingår i: eLIFE. - : eLife Sciences Publications Ltd. - 2050-084X. ; 12
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Tidskriftsartikel (refereegranskat)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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- Menkveld, Albert J., et al.
(författare)
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Nonstandard Errors
- 2024
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Ingår i: JOURNAL OF FINANCE. - : Wiley-Blackwell. - 0022-1082 .- 1540-6261. ; 79:3, s. 2339-2390
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Tidskriftsartikel (refereegranskat)abstract
- In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty-nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.
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- Delgado, Luis Fernando, et al.
(författare)
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Phylogeny-based comparative genomics of Vibrio vulnificus links genetic traits to pathogenicity
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Vibrio vulnificus is a natural part of the microbiome of brackish waters worldwide. It is also an opportunistic pathogen that can cause severe infections and septicemia via consumption of seafood or through wound infections. The species possess diverse virulence factors, yet its precise disease mechanism remains undefined. Comparative genomics between clinical and environmental isolates offers a means to identify key virulence genes, but the scarcity of environmental isolates for V. vulnificus has constituted a significant limitation. Here we sequenced genomes of 82 V. vulnificus isolates from water, sediment and seagrass surface from stations along the Baltic Sea coast and complemented these with 208 and 117 previously sequenced clinical and environmental genomes, respectively, in a comparative analysis. Phylogenetic reconstruction corroborated earlier analysis with four main lineages forming within the species. Strains from the Baltic Sea region were confined to certain phylogenetic lineages (L4 and sublineages L2c and L2e) whereas clinical and environmental strains were found in all lineages, indicting that the phylogenetic structure of V. vulnificus reflects adaptations to specific environmental conditions rather than pathogenicity. Employing orthologue enrichment analysis in a phylogenetic framework using the PhyloBOTL pipeline developed in this work revealed 58 significantly enriched orthologs in clinical compared to environmental isolates. These orthologs were grouped into 18 co-localisation clusters based on the corresponding genes’ proximity in the genomes. The co-localisation clusters entailed clusters with 1 genes previously linked with pathogenicity in V. vulnificus, such as genes for capsular polysaccharide (CPS) synthesis and biofilm formation, but also clusters with genes not previously associated with virulence in the species. Examples of the latter were genes for pilus biosynthesis of the usher-chaperone (CU) pathway, for spermidine synthesis, and for effector proteins of the Type VI secretion system. Finally we leveraged on the clinically enriched genes to design PCR primers for detection and surveillance of pathogenic V. vulnificus strains.
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