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Using decision fusion methods to improve outbreak detection in disease surveillance
Gaëtan Texier1,2, Rodrigue S Allodji3,4,5, Loty Diop6
1French Armed Forces Center for Epidemiology and Public Health (CESPA), SSA, Camp de Sainte Marthe, 13568, Marseille, France. gaetex1@gmail.com.
BMC Medical Informatics and Decision Making
|March 7, 2019
Summary
Decision fusion (DF) methods significantly improve disease outbreak detection. Bayesian networks offer superior performance, enhancing positive predictive value by 77% compared to individual outbreak detection algorithms (ODAs).
Area of Science:
- Epidemiology
- Computer Science
- Public Health
Background:
- Outbreak detection algorithms (ODAs) individually treat detection as a binary classification task.
- Joint analysis of multiple ODAs transforms outbreak detection into a multi-sensor decision fusion (DF) problem.
Purpose of the Study:
- To evaluate the benefits of using DF methods for fusing multiple ODAs in decision support systems.
- To compare the performance of DF methods against single ODA approaches for disease outbreak detection.
Main Methods:
- Six ODAs were employed, with their daily decisions merged using five DF methods: voting, logistic regression, CART, and Bayesian networks (BN).
- Evaluation utilized standard metrics for accuracy, prediction, and timeliness.
Main Results:
- DF methods incorporating a learning step (BN, logistic regression, CART) achieved the greatest improvement.
- A 77% increase in positive predictive value was observed compared to the best-performing individual ODA.
Conclusions:
- Recommends Bayesian network-based DF for robust disease outbreak identification in systems with multiple ODAs.
- Logistic regression is a viable alternative when a training dataset is available.
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