TY - JOUR
T1 - Using decision fusion methods to improve outbreak detection in disease surveillance
AU - Texier, Gaëtan
AU - Alldoji, Rodrigue S.
AU - Diop, Loty
AU - Meynard, Jean Baptiste
AU - Pellegrin, Liliane
AU - Chaudet, Hervé
N1 - Publisher Copyright:
© 2019 The Author(s).
PY - 2019/3/5
Y1 - 2019/3/5
N2 - Background: 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. Methods: 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. Results: 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). Conclusions: 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.
AB - Background: 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. Methods: 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. Results: 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). Conclusions: 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.
KW - Bayesian network
KW - Decision fusion
KW - Decision making
KW - Decision support system
KW - Disease surveillance system
KW - Outbreak detection
UR - http://www.scopus.com/inward/record.url?scp=85062510929&partnerID=8YFLogxK
U2 - 10.1186/s12911-019-0774-3
DO - 10.1186/s12911-019-0774-3
M3 - Article
C2 - 30837003
AN - SCOPUS:85062510929
SN - 1472-6947
VL - 19
JO - BMC Medical Informatics and Decision Making
JF - BMC Medical Informatics and Decision Making
IS - 1
M1 - 38
ER -