Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Modeling outbreak data: Analysis of a 2012 ebola virus disease epidemic in drc

Full metadata record
DC Field Value Language
dc.contributor.authorChoi, B.-
dc.contributor.authorBusch, S.-
dc.contributor.authorKazadi, D.-
dc.contributor.authorKebela, B.-
dc.contributor.authorOkitolonda, E.-
dc.contributor.authorDai, Y.-
dc.contributor.authorLumpkin, R.M.-
dc.contributor.authorBukhsh, W.R.K.-
dc.contributor.authorSaucedo, O.-
dc.contributor.authorYotebieng, M.-
dc.contributor.authorTien, J.-
dc.contributor.authorKenah, E.B.-
dc.contributor.authorRempala, G.A.-
dc.date.accessioned2021-09-02T01:16:54Z-
dc.date.available2021-09-02T01:16:54Z-
dc.date.created2021-06-17-
dc.date.issued2019-
dc.identifier.issn1314-684X-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/70738-
dc.description.abstractWe describe two approaches to modeling data from a small to moderate-sized epidemic outbreak. The first approach is based on a branching process approximation and direct analysis of the transmission network, whereas the second one is based on a survival model derived from the classical SIR equations with no explicit transmission information. We compare these approaches using data from a 2012 outbreak of Ebola virus disease caused by Bundibugyo ebolavirus in city of Isiro, Democratic Republic of the Congo. The branching process model allows for a direct comparison of disease transmission across different environments, such as the general community or the Ebola treatment unit. However, the survival model appears to yield parameter estimates with more accuracy and better precision in some circumstances. © 2019 Dimitrova et al.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherBiomath Forum-
dc.titleModeling outbreak data: Analysis of a 2012 ebola virus disease epidemic in drc-
dc.typeArticle-
dc.contributor.affiliatedAuthorChoi, B.-
dc.identifier.doi10.11145/j.biomath.2019.10.037-
dc.identifier.scopusid2-s2.0-85074631561-
dc.identifier.bibliographicCitationBiomath, v.8, no.2-
dc.relation.isPartOfBiomath-
dc.citation.titleBiomath-
dc.citation.volume8-
dc.citation.number2-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordAuthorBranching process-
dc.subject.keywordAuthorMarkov Chain Monte-Carlo methods-
dc.subject.keywordAuthorParameter estimation-
dc.subject.keywordAuthorSurvival dynamical system-
Files in This Item
There are no files associated with this item.
Appears in
Collections
Graduate School > Department of Economics and Statistics > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetrics

Total Views & Downloads

BROWSE