FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4220757792> ?p ?o ?g. }

• W4220757792 endingPage "e0266096" @default.
• W4220757792 startingPage "e0266096" @default.
• W4220757792 abstract "A combined forecast from multiple models is typically more accurate than an individual forecast, but there are few examples of studies of combining in infectious disease forecasting. We investigated the accuracy of different ways of combining interval forecasts of weekly incident and cumulative coronavirus disease-2019 (COVID-19) mortality.We considered weekly interval forecasts, for 1- to 4-week prediction horizons, with out-of-sample periods of approximately 18 months ending on 8 January 2022, for multiple locations in the United States, using data from the COVID-19 Forecast Hub. Our comparison involved simple and more complex combining methods, including methods that involve trimming outliers or performance-based weights. Prediction accuracy was evaluated using interval scores, weighted interval scores, skill scores, ranks, and reliability diagrams.The weighted inverse score and median combining methods performed best for forecasts of incident deaths. Overall, the leading inverse score method was 12% better than the mean benchmark method in forecasting the 95% interval and, considering all interval forecasts, the median was 7% better than the mean. Overall, the median was the most accurate method for forecasts of cumulative deaths. Compared to the mean, the median's accuracy was 65% better in forecasting the 95% interval, and 43% better considering all interval forecasts. For all combining methods except the median, combining forecasts from only compartmental models produced better forecasts than combining forecasts from all models.Combining forecasts can improve the contribution of probabilistic forecasting to health policy decision making during epidemics. The relative performance of combining methods depends on the extent of outliers and the type of models in the combination. The median combination has the advantage of being robust to outlying forecasts. Our results support the Hub's use of the median and we recommend further investigation into the use of weighted methods." @default.
• W4220757792 created "2022-04-03" @default.
• W4220757792 creator A5048147657 @default.
• W4220757792 creator A5078168355 @default.
• W4220757792 date "2022-03-29" @default.
• W4220757792 modified "2023-10-16" @default.
• W4220757792 title "Interval forecasts of weekly incident and cumulative COVID-19 mortality in the United States: A comparison of combining methods" @default.
• W4220757792 cites W2021510303 @default.
• W4220757792 cites W2025720061 @default.
• W4220757792 cites W2033501063 @default.
• W4220757792 cites W2068448163 @default.
• W4220757792 cites W2129487167 @default.
• W4220757792 cites W2137918734 @default.
• W4220757792 cites W2554640988 @default.
• W4220757792 cites W2767745493 @default.
• W4220757792 cites W2895438781 @default.
• W4220757792 cites W2895794263 @default.
• W4220757792 cites W2958944865 @default.
• W4220757792 cites W2988205563 @default.
• W4220757792 cites W2991460415 @default.
• W4220757792 cites W3003749070 @default.
• W4220757792 cites W3013080757 @default.
• W4220757792 cites W3013360115 @default.
• W4220757792 cites W3016460100 @default.
• W4220757792 cites W3016881422 @default.
• W4220757792 cites W3025512159 @default.
• W4220757792 cites W3031381838 @default.
• W4220757792 cites W3032337658 @default.
• W4220757792 cites W3046253915 @default.
• W4220757792 cites W3048757009 @default.
• W4220757792 cites W3080376427 @default.
• W4220757792 cites W3085464968 @default.
• W4220757792 cites W3087139467 @default.
• W4220757792 cites W3097549410 @default.
• W4220757792 cites W3122515788 @default.
• W4220757792 cites W3123965474 @default.
• W4220757792 cites W3125503947 @default.
• W4220757792 cites W3125857399 @default.
• W4220757792 cites W3136360158 @default.
• W4220757792 cites W3174685221 @default.
• W4220757792 cites W3196188801 @default.
• W4220757792 cites W3202518179 @default.
• W4220757792 cites W4200604684 @default.
• W4220757792 cites W4250639944 @default.
• W4220757792 cites W4292671038 @default.
• W4220757792 cites W4313429334 @default.
• W4220757792 doi "https://doi.org/10.1371/journal.pone.0266096" @default.
• W4220757792 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35349605" @default.
• W4220757792 hasPublicationYear "2022" @default.
• W4220757792 type Work @default.
• W4220757792 citedByCount "3" @default.
• W4220757792 countsByYear W42207577922022 @default.
• W4220757792 countsByYear W42207577922023 @default.
• W4220757792 crossrefType "journal-article" @default.
• W4220757792 hasAuthorship W4220757792A5048147657 @default.
• W4220757792 hasAuthorship W4220757792A5078168355 @default.
• W4220757792 hasBestOaLocation W42207577921 @default.
• W4220757792 hasConcept C103402496 @default.
• W4220757792 hasConcept C105795698 @default.
• W4220757792 hasConcept C107673813 @default.
• W4220757792 hasConcept C114614502 @default.
• W4220757792 hasConcept C120954023 @default.
• W4220757792 hasConcept C121117317 @default.
• W4220757792 hasConcept C149782125 @default.
• W4220757792 hasConcept C170061395 @default.
• W4220757792 hasConcept C185798385 @default.
• W4220757792 hasConcept C205649164 @default.
• W4220757792 hasConcept C2778067643 @default.
• W4220757792 hasConcept C33923547 @default.
• W4220757792 hasConcept C41008148 @default.
• W4220757792 hasConcept C44249647 @default.
• W4220757792 hasConcept C58640448 @default.
• W4220757792 hasConcept C79337645 @default.
• W4220757792 hasConceptScore W4220757792C103402496 @default.
• W4220757792 hasConceptScore W4220757792C105795698 @default.
• W4220757792 hasConceptScore W4220757792C107673813 @default.
• W4220757792 hasConceptScore W4220757792C114614502 @default.
• W4220757792 hasConceptScore W4220757792C120954023 @default.
• W4220757792 hasConceptScore W4220757792C121117317 @default.
• W4220757792 hasConceptScore W4220757792C149782125 @default.
• W4220757792 hasConceptScore W4220757792C170061395 @default.
• W4220757792 hasConceptScore W4220757792C185798385 @default.
• W4220757792 hasConceptScore W4220757792C205649164 @default.
• W4220757792 hasConceptScore W4220757792C2778067643 @default.
• W4220757792 hasConceptScore W4220757792C33923547 @default.
• W4220757792 hasConceptScore W4220757792C41008148 @default.
• W4220757792 hasConceptScore W4220757792C44249647 @default.
• W4220757792 hasConceptScore W4220757792C58640448 @default.
• W4220757792 hasConceptScore W4220757792C79337645 @default.
• W4220757792 hasIssue "3" @default.
• W4220757792 hasLocation W42207577921 @default.
• W4220757792 hasLocation W42207577922 @default.
• W4220757792 hasLocation W42207577923 @default.
• W4220757792 hasLocation W42207577924 @default.
• W4220757792 hasLocation W42207577925 @default.
• W4220757792 hasOpenAccess W4220757792 @default.
• W4220757792 hasPrimaryLocation W42207577921 @default.
• W4220757792 hasRelatedWork W1592318356 @default.

• next