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首页> 外文期刊>BMC Medical Informatics and Decision Making >Sequential detection of influenza epidemics by the Kolmogorov-Smirnov test
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Sequential detection of influenza epidemics by the Kolmogorov-Smirnov test

机译：通过Kolmogorov-Smirnov检验顺序检测流感流行病

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Background Influenza is a well known and common human respiratory infection, causing significant morbidity and mortality every year. Despite Influenza variability, fast and reliable outbreak detection is required for health resource planning. Clinical health records, as published by the Diagnosticat database in Catalonia, host useful data for probabilistic detection of influenza outbreaks. Methods This paper proposes a statistical method to detect influenza epidemic activity. Non-epidemic incidence rates are modeled against the exponential distribution, and the maximum likelihood estimate for the decaying factor λ is calculated. The sequential detection algorithm updates the parameter as new data becomes available. Binary epidemic detection of weekly incidence rates is assessed by Kolmogorov-Smirnov test on the absolute difference between the empirical and the cumulative density function of the estimated exponential distribution with significance level 0 ≤ α ≤ 1. Results The main advantage with respect to other approaches is the adoption of a statistically meaningful test, which provides an indicator of epidemic activity with an associated probability. The detection algorithm was initiated with parameter λ0 = 3.8617 estimated from the training sequence (corresponding to non-epidemic incidence rates of the 2008-2009 influenza season) and sequentially updated. Kolmogorov-Smirnov test detected the following weeks as epidemic for each influenza season: 50?10 (2008-2009 season), 38?50 (2009-2010 season), weeks 50?9 (2010-2011 season) and weeks 3 to 12 for the current 2011-2012 season. Conclusions Real medical data was used to assess the validity of the approach, as well as to construct a realistic statistical model of weekly influenza incidence rates in non-epidemic periods. For the tested data, the results confirmed the ability of the algorithm to detect the start and the end of epidemic periods. In general, the proposed test could be applied to other data sets to quickly detect influenza outbreaks. The sequential structure of the test makes it suitable for implementation in many platforms at a low computational cost without requiring to store large data sets.
机译：背景技术流行性感冒是一种众所周知的常见的人类呼吸道感染，每年导致大量的发病和死亡。尽管流感多变，但卫生资源规划仍需要快速可靠的爆发检测。由加泰罗尼亚Diagnosticat数据库发布的临床健康记录包含有用的数据，可用于概率性检测流感爆发。方法本文提出了一种统计方法来检测流行性感冒的流行。针对指数分布对非流行病的发生率建模，并计算出衰减因子λ的最大似然估计。当新数据可用时，顺序检测算法将更新参数。通过Kolmogorov-Smirnov检验，对显着性水平为0≤α≤1的估计指数分布的经验和累积密度函数之间的绝对差进行评估，以评估每周发病率的二元流行病。结果相对于其他方法，主要优点是采用具有统计意义的检验，该检验提供了具有相关概率的流行病指标。从训练序列（与2008-2009流感季节的非流行发病率相对应）估计的参数λ 0 = 3.8617启动检测算法，并依次更新。 Kolmogorov-Smirnov测试在每个流感季节的以下几周内发现了流行病：50？10（2008-2009季节），38？50（2009-2010季节），50？9周（2010-2011季节）和3至12周当前的2011-2012赛季。结论使用真实的医学数据评估了该方法的有效性，并构建了非流行期间每周流感发病率的现实统计模型。对于测试数据，结果证实了该算法检测流行病时期开始和结束的能力。通常，建议的测试可以应用于其他数据集，以快速检测流感爆发。测试的顺序结构使其适合在许多平台上以较低的计算成本实现，而无需存储大量数据集。

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