Correctly and quickly identifying disease patterns and clusters is a vital aspect of public health and epidemiology so that disease outbreaks can be mitigated as effectively as possible. The circular scan method is one of the most commonly used methods for detecting disease outbreaks and clusters in retrospective and prospective disease surveillance. The circular scan method requires a population upper bound in order to construct the set of candidate zones to be scanned, which is usually set to 50% of the total population. The performance of the circular scan method is affected by the choice of the population upper bound, and choosing an upper bound different from the default value can improve the method's performance. Recently, the Gini coefficient based on the Lorenz curve, which was originally used in economics, was proposed to determine a better population upper bound. We present the elbow method, a new method for choosing the population upper bound, which seeks to address some of the limitations of the Gini-based method while improving the performance of the circular scan method over the default value. To evaluate the performance of the proposed approach, we evaluate the sensitivity and positive predictive value of the circular scan method for publicly-available benchmark data for the default value, the Gini coefficient method, and the elbow method.

中文翻译：

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估计回顾性疾病监测中扫描方法的最佳人群上限

正确快速地识别疾病模式和集群是公共卫生和流行病学的一个重要方面，以便尽可能有效地缓解疾病爆发。循环扫描法是回顾性和前瞻性疾病监测中检测疾病暴发和聚集性最常用的方法之一。循环扫描方法需要一个人口上限，以构建要扫描的候选区域集，通常设置为总人口的 50%。循环扫描方法的性能受总体上界的选择影响，选择不同于默认值的上界可以提高方法的性能。最近，基于洛伦兹曲线的基尼系数，最初用在经济学中，被提议来确定一个更好的人口上限。我们提出了肘部方法，这是一种选择总体上限的新方法，旨在解决基于 Gini 方法的一些限制，同时提高循环扫描方法在默认值上的性能。为了评估所提出方法的性能，我们评估了循环扫描方法对默认值、基尼系数方法和肘部方法的公开可用基准数据的敏感性和阳性预测值。