Date Published: July 17, 2017
Publisher: Public Library of Science
Author(s): Gabriel Bédubourg, Yann Le Strat, Donald R. Olson.
The objective of this paper is to evaluate a panel of statistical algorithms for temporal outbreak detection. Based on a large dataset of simulated weekly surveillance time series, we performed a systematic assessment of 21 statistical algorithms, 19 implemented in the R package surveillance and two other methods. We estimated false positive rate (FPR), probability of detection (POD), probability of detection during the first week, sensitivity, specificity, negative and positive predictive values and F1-measure for each detection method. Then, to identify the factors associated with these performance measures, we ran multivariate Poisson regression models adjusted for the characteristics of the simulated time series (trend, seasonality, dispersion, outbreak sizes, etc.). The FPR ranged from 0.7% to 59.9% and the POD from 43.3% to 88.7%. Some methods had a very high specificity, up to 99.4%, but a low sensitivity. Methods with a high sensitivity (up to 79.5%) had a low specificity. All methods had a high negative predictive value, over 94%, while positive predictive values ranged from 6.5% to 68.4%. Multivariate Poisson regression models showed that performance measures were strongly influenced by the characteristics of time series. Past or current outbreak size and duration strongly influenced detection performances.
Public health surveillance is the ongoing, systematic collection, analysis, interpretation, and dissemination of data for use in public health action to reduce morbidity and mortality of health-related events and to improve health . One of the objectives of health surveillance is outbreak detection, which is crucial to enabling rapid investigation and implementation of control measures . The threat of bioterrorism has stimulated interest in improving health surveillance systems for early detection of outbreaks [3, 4] as have natural disasters and humanitarian crises, such as earthquakes or the 2005 tsunami, and the recent emergence or reemergence of infectious diseases such as Middle East Respiratory Syndrome due to New Coronavirus (MERS-CoV) in 2012  or Ebola in West Africa in 2014 .
We simulated data following the approach proposed by Noufaily et al. .
We presented a systematic assessment of the performance of 21 outbreak detection algorithms using a simulated dataset. One advantage of a simulation study for outbreak detection methods benchmarking is the a priori knowledge of the occurrence of outbreaks, which enables the developpment of a real “gold standard”. Some authors have already proposed that simulation studies be used to assess outbreak detection methods [18, 19, 23], and others have suggested adding simulated outbreaks to real surveillance data baselines [16, 24, 25], but without proposing a systematic assessment of the performance of a broad range of outbreak detection methods. Choi et al.  proposed such a study design based on the daily simulation method proposed by Hutwagner et al.  but do not study the influence of past outbreaks or time series characteristics (frequency, variance, secular trends, seasonalities, etc.), on methods performance.