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

Boseung Choi, Sydney Busch, Dieudonne Kazadi, Benoit Kebela, Emile Okitolonda, Yi Dai, Robert M. Lumpkin, Wasiur Rahman Khuda Bukhsh, Omar Saucedo, Marcel Yotebieng, Joseph Tien, Eben B. Kenah, Grzegorz A. Rempala

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Article number1910037
JournalBiomath
Volume8
Issue number2
DOIs
StatePublished - 2019

Keywords

  • Branching process
  • Markov Chain Monte-Carlo methods
  • Parameter estimation
  • Survival dynamical system

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Agricultural and Biological Sciences (miscellaneous)
  • Applied Mathematics

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    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. (2019). Modeling outbreak data: Analysis of a 2012 ebola virus disease epidemic in drc. Biomath, 8(2), [1910037]. https://doi.org/10.11145/j.biomath.2019.10.037