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Comparison of combination methods to create calibrated ensemble forecasts for seasonal influenza in the U.S.

Statistics in Medicine(2023)10.1002/sim.9884Source: DataRank Database

Comparison of combination methods to create calibrated ensemble forecasts for seasonal influenza in the U.S. is a research paper published in Statistics in Medicine (2023). On theSindex it has a DataRank of 0.

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (2 grants)Impact (FWCI 0.00)OA: hybridTopics (28)IDs (PubMed, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (4)

Evan L Ray ORCID,Thomas McAndrew ORCID,Nicholas G. Reich ORCID,Nutcha Wattanachit ORCID

Live enrichment

PubMed:37648218 OpenAlex:https://openalex.org/W4226512887

Centers for Disease Control and Prevention

Grant: 1U01IP001122

National Institute of General Medical Sciences

Grant: R35GM119582

FWCI

0.00

Citation Percentile

0.0%

hybridLicense: cc-by

journalhttps://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/sim.9884

publisherhttps://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/sim.9884

publisherhttps://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.9884

journalhttps://doi.org/10.1002/sim.9884

repositoryhttps://pubmed.ncbi.nlm.nih.gov/37648218

Fields of Study

Influenza Virus Research StudiesCOVID-19 epidemiological studiesData-Driven Disease Surveillance

MeSH Terms

ForecastingHumansInfluenza, HumanProspective StudiesRetrospective StudiesSeasonsModels, Statistical

Keywords

Ensemble forecastingPoolingCalibrationEnsemble learningComputer scienceEconometricsLinear modelProbabilistic logicSeasonal influenzaParametric statisticsStatisticsEnvironmental scienceMathematicsMachine learningArtificial intelligenceCoronavirus disease 2019 (COVID-19)MedicineDisease