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01.12.2019 | Research article | Ausgabe 1/2019 Open Access

BMC Medical Informatics and Decision Making 1/2019

Using decision fusion methods to improve outbreak detection in disease surveillance

BMC Medical Informatics and Decision Making > Ausgabe 1/2019
Gaëtan Texier, Rodrigue S. Allodji, Loty Diop, Jean-Baptiste Meynard, Liliane Pellegrin, Hervé Chaudet
Wichtige Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1186/​s12911-019-0774-3) contains supplementary material, which is available to authorized users.
The original version of this article was revised: the original article contained an error in an author name. It should be Allodji not Alldoji.
A correction to this article is available online at https://​doi.​org/​10.​1186/​s12911-019-0802-3.



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.


This study evaluated the benefit for a decisions support system of using DF methods (fusing multiple ODA decisions) compared to using a single method of outbreak detection. For each day, we merged the decisions of six ODAs using 5 DF methods (two voting methods, logistic regression, CART and Bayesian network - BN). Classical metrics of accuracy, prediction and timelines were used during the evaluation steps.


In our results, we observed the greatest gain (77%) in positive predictive value compared to the best ODA if we used DF methods with a learning step (BN, logistic regression, and CART).


To identify disease outbreaks in systems using several ODAs to analyze surveillance data, we recommend using a DF method based on a Bayesian network. This method is at least equivalent to the best of the algorithms considered, regardless of the situation faced by the system. For those less familiar with this kind of technique, we propose that logistic regression be used when a training dataset is available.
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