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Application of Bayesian spatial-temporal models for estimating unrecognized COVID-19 deaths in the United States

Spatial Statistics(2022)10.1016/j.spasta.2021.100584Source: DataRank Database

Application of Bayesian spatial-temporal models for estimating unrecognized COVID-19 deaths in the United States is a research paper published in Spatial Statistics (2022). On theSindex it has a DataRank of 0. It has been cited 18 times.

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Open Access18 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (3 grants)OA: bronzeTopics (7)IDs (PubMed, PMC)Annotations (1)SDGs (1)

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Context only (not used in score)

Findable (1/2)

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Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (4)

Howard H. Chang ORCID,A. Danielle Iuliano ORCID,Carrie Reed ORCID,Yuzi Zhang ORCID

Live enrichment

PubMed:35013705 PMC:PMC8730676

National Institutes of Health

Grant: R01AI125842

National Institutes of Health

Grant: 1R01AI125842-01A1

Spatio-temporal data integration methods for infectious disease surveillance

Centers for Disease Control and Prevention

bronzeLicense: Elsevier TDM

publisherhttps://api.elsevier.com/content/article/PII:S2211675321000798?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S2211675321000798?httpAccept=text/plain

Europe_PMChttps://europepmc.org/articles/PMC8730676

Europe_PMChttps://europepmc.org/articles/PMC8730676?pdf=render

https://doi.org/10.1016/j.spasta.2021.100584

Fields of Study

03 medical and health sciences0302 clinical medicine

Keywords

Excess MortalityBayesian Hierarchical ModelingCovid-19Spatial–Temporal ModelingArticle

Sustainable Development Goals

SDG 3: 3. Good health

Application of Bayesian spatial-temporal models for estimating unrecognized COVID-19 deaths in the United States

FAIR Checklist

Authors (4)

Funding (3 grants)

Open Access

Topics

Annotations (Europe PMC)