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Published on in Vol 8, No 1 (2016):

Performance of Early Outbreak Detection Algorithms in Public Health Surveillance from a Simulation Study

Performance of Early Outbreak Detection Algorithms in Public Health Surveillance from a Simulation Study

Performance of Early Outbreak Detection Algorithms in Public Health Surveillance from a Simulation Study

Authors of this article:

Gabriel Bedubourg ;   Yann Le Strat
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    We performed a simulation study in order to evaluate performance of 8 algorithms used in health surveillance for early outbreak detection. Each method was evaluated through its false positive rate (FPR) and its probability of detection (POD: at least one alarm during the outbreak period), for the different scenarios and outbreak sizes. Some methods have presented POD>80% and FPR<20% for the largest simulated outbreaks. For other algorithms, we observed heterogeneous performances according to simulation scenarios and outbreak sizes. Other performance criteria need to be proposed in order to improve the choice of algorithms to be implemented in health surveillance.