Evaluation and comparison of statistical methods for early temporal detection of outbreaks: A simulation-based study.

ABSTRACT: 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 fro

SUBMITTER: Bedubourg G 

PROVIDER: S-EPMC5513450 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature