id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-353596-8iqjugcx	Bédubourg, Gabriel	Evaluation and comparison of statistical methods for early temporal detection of outbreaks: A simulation-based study	2017-07-17		.txt	text/plain	5603	313	53	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 F(1)-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 objective of this paper is to evaluate the performance of 21 statistical methods applied to large simulated datasets for outbreak detection in weekly health surveillance. Table 2 presents averaged FPR, specificity, POD, POD1week, sensitivity, negative predictive value, positive predictive value and F 1 -measure for all 42 scenarios and all past and current outbreak amplitude and duration and for α = 0.01.	./cache/cord-353596-8iqjugcx.txt	./txt/cord-353596-8iqjugcx.txt