Evaluation and comparison of statistical methods for early temporal detection of outbreaks: A simulation-based study.
Evaluation and comparison of statistical methods for early temporal detection of outbreaks: A simulation-based study.
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DOI：
10.1371/journal.pone.0181227
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Le Strat Y
中科院分区：
文献类型：
--
作者：
Bédubourg G;Le Strat Y
本文的目的是评估一组用于时间爆发检测的统计算法。基于模拟每周监测时间序列的大型数据集，我们对21种统计算法进行了系统评估，其中19种在R包监测中实施，另外两种方法。我们估计了每种检测方法的假阳性率（FPR）、检出概率（POD）、第一周检出概率、敏感性、特异性、阴性和阳性预测值以及f1测量值。然后，为了确定与这些绩效指标相关的因素，我们运行了针对模拟时间序列特征（趋势、季节性、离散性、爆发规模等）进行调整的多变量泊松回归模型。FPR为0.7% ~ 59.9%，POD为43.3% ~ 88.7%。有些方法特异性很高，可达99.4%，但敏感性较低。灵敏度高（达79.5%）的方法特异性较低。阴性预测值均在94%以上，阳性预测值在6.5% ~ 68.4%之间。多元泊松回归模型表明，绩效指标受到时间序列特征的强烈影响。过去或当前的爆发大小和持续时间严重影响检测性能。
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.
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