Using decision fusion methods to improve outbreak detection in disease surveillance
When outbreak detection algorithms (ODAs) are considered individually, the task of outbreak detection can be seen as a classification problem and the ODA as a sensor providing a binary decision (outbreak yes or no) for each day of surveillance. When they are considered jointly (in cases where severa...
| Autores principales: | , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2019
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/146057 |
| _version_ | 1855524849383899136 |
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| author | Texier, Gaëtan Allodji, Rodrigue S. Diop, Loty Meynard, Jean-Baptiste Pellegrin, Liliane Chaudet, Hervé |
| author_browse | Allodji, Rodrigue S. Chaudet, Hervé Diop, Loty Meynard, Jean-Baptiste Pellegrin, Liliane Texier, Gaëtan |
| author_facet | Texier, Gaëtan Allodji, Rodrigue S. Diop, Loty Meynard, Jean-Baptiste Pellegrin, Liliane Chaudet, Hervé |
| author_sort | Texier, Gaëtan |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | When outbreak detection algorithms (ODAs) are considered individually, the task of outbreak detection can be seen as a classification problem and the ODA as a sensor providing a binary decision (outbreak yes or no) for each day of surveillance. When they are considered jointly (in cases where several ODAs analyze the same surveillance signal), the outbreak detection problem should be treated as a decision fusion (DF) problem of multiple sensors. |
| format | Journal Article |
| id | CGSpace146057 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1460572025-12-08T10:29:22Z Using decision fusion methods to improve outbreak detection in disease surveillance Texier, Gaëtan Allodji, Rodrigue S. Diop, Loty Meynard, Jean-Baptiste Pellegrin, Liliane Chaudet, Hervé algorithms health decision-support systems decision fusion capacity development bayesian theory decision making disease surveillance When outbreak detection algorithms (ODAs) are considered individually, the task of outbreak detection can be seen as a classification problem and the ODA as a sensor providing a binary decision (outbreak yes or no) for each day of surveillance. When they are considered jointly (in cases where several ODAs analyze the same surveillance signal), the outbreak detection problem should be treated as a decision fusion (DF) problem of multiple sensors. 2019-08-13 2024-06-21T09:05:42Z 2024-06-21T09:05:42Z Journal Article https://hdl.handle.net/10568/146057 en Open Access Springer Texier, Gaëtan; Allodji, Rodrigue S.; Diop, Loty; Meynard, Jean-Baptiste; Pellegrin, Liliane; and Chaudet, Hervé. 2019. Using decision fusion methods to improve outbreak detection in disease surveillance. BMC Medical Informatics and Decision Making 19: 38. https://doi.org/10.1186/s12911-019-0774-3 |
| spellingShingle | algorithms health decision-support systems decision fusion capacity development bayesian theory decision making disease surveillance Texier, Gaëtan Allodji, Rodrigue S. Diop, Loty Meynard, Jean-Baptiste Pellegrin, Liliane Chaudet, Hervé Using decision fusion methods to improve outbreak detection in disease surveillance |
| title | Using decision fusion methods to improve outbreak detection in disease surveillance |
| title_full | Using decision fusion methods to improve outbreak detection in disease surveillance |
| title_fullStr | Using decision fusion methods to improve outbreak detection in disease surveillance |
| title_full_unstemmed | Using decision fusion methods to improve outbreak detection in disease surveillance |
| title_short | Using decision fusion methods to improve outbreak detection in disease surveillance |
| title_sort | using decision fusion methods to improve outbreak detection in disease surveillance |
| topic | algorithms health decision-support systems decision fusion capacity development bayesian theory decision making disease surveillance |
| url | https://hdl.handle.net/10568/146057 |
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