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

Authors
Choi, B.Busch, S.Kazadi, D.Kebela, B.Okitolonda, E.Dai, Y.Lumpkin, R.M.Bukhsh, W.R.K.Saucedo, O.Yotebieng, M.Tien, J.Kenah, E.B.Rempala, G.A.
Issue Date
2019
Publisher
Biomath Forum
Keywords
Branching process; Markov Chain Monte-Carlo methods; Parameter estimation; Survival dynamical system
Citation
Biomath, v.8, no.2
Indexed
SCOPUS
Journal Title
Biomath
Volume
8
Number
2
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/70738
DOI
10.11145/j.biomath.2019.10.037
ISSN
1314-684X
Abstract
We 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.
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Graduate School > Department of Economics and Statistics > 1. Journal Articles

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