Modeling outbreak data: Analysis of a 2012 Ebola virus disease epidemic in DRC
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...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Bulgarian Academy of Sciences, Institute of Mathematics and Informatics
2019-10-01
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| Series: | Biomath |
| Subjects: | |
| Online Access: | http://www.biomathforum.org/biomath/index.php/biomath/article/view/1316 |
| _version_ | 1797762646306455552 |
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| author | Boseung Choi Sydney Busch Dieudonne Kazadi Benoit Kebela Emile Okitolonda Yi Dai Robert M Lumpkin Wasiur Rahman Khuda Bukhsh Omar Saucedo Marcel Yotebieng Joe Tien Eben B Kenah Grzegorz A Rempala |
| author_facet | Boseung Choi Sydney Busch Dieudonne Kazadi Benoit Kebela Emile Okitolonda Yi Dai Robert M Lumpkin Wasiur Rahman Khuda Bukhsh Omar Saucedo Marcel Yotebieng Joe Tien Eben B Kenah Grzegorz A Rempala |
| author_sort | Boseung Choi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T19:31:24Z |
| format | Article |
| id | doaj.art-41532b5d32bb4f2b813d3b55b7bba66c |
| institution | Directory Open Access Journal |
| issn | 1314-684X 1314-7218 |
| language | English |
| last_indexed | 2024-03-12T19:31:24Z |
| publishDate | 2019-10-01 |
| publisher | Bulgarian Academy of Sciences, Institute of Mathematics and Informatics |
| record_format | Article |
| series | Biomath |
| spelling | doaj.art-41532b5d32bb4f2b813d3b55b7bba66c2023-08-02T04:31:30ZengBulgarian Academy of Sciences, Institute of Mathematics and InformaticsBiomath1314-684X1314-72182019-10-018210.11145/j.biomath.2019.10.037834Modeling outbreak data: Analysis of a 2012 Ebola virus disease epidemic in DRCBoseung Choi0Sydney Busch1Dieudonne Kazadi2Benoit Kebela3Emile Okitolonda4Yi Dai5Robert M Lumpkin6Wasiur Rahman Khuda Bukhsh7Omar Saucedo8Marcel Yotebieng9Joe Tien10Eben B Kenah11Grzegorz A Rempala12Department of National Statistics, Korea University Sejoung Campus, Seoul, Republic of KoreaAugsburg UniversityMinistry of Health, Democratic Republic of Kongo, Kinshasa, DRCDepartment of Public Health, University of Kinshasa, Kinshasa, DRCDepartment of Public Health, University of Kinshasa, Kinshasa, DRCDivision of Biostatistics, The Ohio State UniversityDepartment of Statistics, The Ohio State UniversityMathematical Biosciences Institute, The Ohio State UniversityMathematical Biosciences Institute, The Ohio State UniversityDivision of Epidemiology The Ohio State UniversityDepartment of Mathematics The Ohio State UniversityDivision of Biostatistics The Ohio State UniversityDepartment of Mathematics, Division of Biostatistics and Mathematical Biosciences Institute, The Ohio State UniversityWe 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.http://www.biomathforum.org/biomath/index.php/biomath/article/view/1316parameter estimation, branching process, markov chain monte-carlo methods, survival dynamical system |
| spellingShingle | Boseung Choi Sydney Busch Dieudonne Kazadi Benoit Kebela Emile Okitolonda Yi Dai Robert M Lumpkin Wasiur Rahman Khuda Bukhsh Omar Saucedo Marcel Yotebieng Joe Tien Eben B Kenah Grzegorz A Rempala Modeling outbreak data: Analysis of a 2012 Ebola virus disease epidemic in DRC Biomath parameter estimation, branching process, markov chain monte-carlo methods, survival dynamical system |
| title | Modeling outbreak data: Analysis of a 2012 Ebola virus disease epidemic in DRC |
| title_full | Modeling outbreak data: Analysis of a 2012 Ebola virus disease epidemic in DRC |
| title_fullStr | Modeling outbreak data: Analysis of a 2012 Ebola virus disease epidemic in DRC |
| title_full_unstemmed | Modeling outbreak data: Analysis of a 2012 Ebola virus disease epidemic in DRC |
| title_short | Modeling outbreak data: Analysis of a 2012 Ebola virus disease epidemic in DRC |
| title_sort | modeling outbreak data analysis of a 2012 ebola virus disease epidemic in drc |
| topic | parameter estimation, branching process, markov chain monte-carlo methods, survival dynamical system |
| url | http://www.biomathforum.org/biomath/index.php/biomath/article/view/1316 |
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