Modeling outbreak data: Analysis of a 2012 Ebola virus disease epidemic in DRC

Biomath

卷:8

Sydney Busch¹  Boseung Choi²  Joe Tien³  Grzegorz A Rempala⁴  Emile Okitolonda⁵  Benoit Kebela⁵  Robert M Lumpkin⁶  Eben B Kenah⁷  Yi Dai⁸  Marcel Yotebieng⁹  Wasiur Rahman Khuda Bukhsh¹⁰  Omar Saucedo¹⁰  Dieudonne Kazadi¹¹ 

[1] Augsburg University;

[2] Department ofNational Statistics, Korea University Sejoung Campus, Seoul, Republic of Korea;

[3] Department of MathematicsThe Ohio State University;

[4] Department of Mathematics, Division of Biostatistics andMathematical Biosciences Institute,The Ohio State University;

[5] Department of Public Health, University of Kinshasa, Kinshasa, DRC;

[6] Department of Statistics, The Ohio State University;

[7] Division of BiostatisticsThe Ohio State University;

[8] Division of Biostatistics, The Ohio State University;

[9] Division of EpidemiologyThe Ohio State University;

[10] Mathematical Biosciences Institute, The Ohio State University;

[11] Ministry of Health, Democratic Republic of Kongo, Kinshasa, DRC;

关键词: parameter estimation, branching process, markov chain monte-carlo methods, survival dynamical system;   

DOI  :  10.11145/j.biomath.2019.10.037

来源: DOAJ

【 摘 要 】

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, Demo- cratic 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.

【 授权许可】

Unknown