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首页> 美国卫生研究院文献>International Journal of Environmental Research and Public Health >A Simulation-Based Study on the Comparison of Statistical and Time Series Forecasting Methods for Early Detection of Infectious Disease Outbreaks
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A Simulation-Based Study on the Comparison of Statistical and Time Series Forecasting Methods for Early Detection of Infectious Disease Outbreaks

机译：基于模拟的统计分析和时间序列预测方法在传染病暴发早期检测中的比较研究

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摘要

Early detection of infectious disease outbreaks is one of the important and significant issues in syndromic surveillance systems. It helps to provide a rapid epidemiological response and reduce morbidity and mortality. In order to upgrade the current system at the Korea Centers for Disease Control and Prevention (KCDC), a comparative study of state-of-the-art techniques is required. We compared four different temporal outbreak detection algorithms: the CUmulative SUM (CUSUM), the Early Aberration Reporting System (EARS), the autoregressive integrated moving average (ARIMA), and the Holt-Winters algorithm. The comparison was performed based on not only 42 different time series generated taking into account trends, seasonality, and randomly occurring outbreaks, but also real-world daily and weekly data related to diarrhea infection. The algorithms were evaluated using different metrics. These were namely, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score, symmetric mean absolute percent error (sMAPE), root-mean-square error (RMSE), and mean absolute deviation (MAD). Although the comparison results showed better performance for the EARS C3 method with respect to the other algorithms, despite the characteristics of the underlying time series data, Holt–Winters showed better performance when the baseline frequency and the dispersion parameter values were both less than 1.5 and 2, respectively.
机译：早期发现传染病暴发是症状监测系统中重要且重要的问题之一。它有助于提供快速的流行病学反应，并降低发病率和死亡率。为了升级韩国疾病控制与预防中心（KCDC）的当前系统，需要对最新技术进行比较研究。我们比较了四种不同的时间爆发检测算法：累积总和（CUSUM），早期像差报告系统（EARS），自回归综合移动平均值（ARIMA）和Holt-Winters算法。进行比较时，不仅基于42个不同的时间序列，这些时间序列考虑了趋势，季节性和随机发生的暴发，而且还考虑了与腹泻感染相关的真实的每日和每周数据。使用不同的指标对算法进行了评估。这些分别是敏感性，特异性，阳性预测值（PPV），阴性预测值（NPV），F1得分，对称平均绝对百分比误差（sMAPE），均方根误差（RMSE）和平均绝对偏差（MAD） ）。尽管比较结果显示EARS C3方法相对于其他算法具有更好的性能，但是尽管具有基础时间序列数据的特性，但是当基线频率和色散参数值均小于1.5和1.5时，Holt–Winters表现出更好的性能。 2，分别。

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