TY - JOUR
T1 - Modeling outbreak data
T2 - Analysis of a 2012 ebola virus disease epidemic in drc
AU - Choi, Boseung
AU - Busch, Sydney
AU - Kazadi, Dieudonne
AU - Kebela, Benoit
AU - Okitolonda, Emile
AU - Dai, Yi
AU - Lumpkin, Robert M.
AU - Bukhsh, Wasiur Rahman Khuda
AU - Saucedo, Omar
AU - Yotebieng, Marcel
AU - Tien, Joseph
AU - Kenah, Eben B.
AU - Rempala, Grzegorz A.
N1 - Funding Information:
This research was partially funded by the US National Science Foundation and the US National Institutes of Health under grants DMS1853587, U54 GM111274 and R01 AI116770. The funds were also received from the Mershon Center for International Security Studies at OSU, the Korea University and the Ohio Five-OSU Summer Undergraduate Research Experience (SURE) Fund. The authors would like to especially thank the Mathematical Biosciences Institute (MBI) at OSU for providing additional resources and logistical help. MBI receives funds from the National Science Foundation under grants DMS1440386 and DMS1757423.
Publisher Copyright:
© 2019 Dimitrova et al.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Branching process
KW - Markov Chain Monte-Carlo methods
KW - Parameter estimation
KW - Survival dynamical system
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U2 - 10.11145/j.biomath.2019.10.037
DO - 10.11145/j.biomath.2019.10.037
M3 - Article
AN - SCOPUS:85074631561
SN - 1314-684X
VL - 8
JO - Biomath
JF - Biomath
IS - 2
M1 - 1910037
ER -
