Taylor's Power Law and the Statistical Modelling of Infectious Disease Surveillance Data
研究了20年间数百种传染病的监测数据，发现方差与均值之间存在幂律关系（泰勒幂律），并探讨了如何利用这种关系改进传染病监测数据的描述性统计建模，包括模型构建、模拟和阈值估计，对疫情检测有重要意义。
Summary Surveillance data collected on several hundred different infectious organisms over 20 years have revealed striking power relationships between their variance and mean in successive time periods. Such patterns are common in ecology, where they are referred to collectively as Taylor's power law. In the paper, these relationships are investigated in detail, with the aim of exploiting them for the descriptive statistical modelling of infectious disease surveillance data. We confirm the existence of variance-to-mean power relationships, with exponent typically between 1 and 2. We investigate skewness-to-mean relationships, which are found broadly to match those expected of Tweedie distributions, and thus confirm the relevance of the Tweedie convergence theorem in this context. We suggest that variance- and skewness-to-mean power laws, when present, should inform statistical modelling of infectious disease surveillance data, notably in descriptive analysis, model building, simulation and interval and threshold estimation, threshold estimation being particularly relevant to outbreak detection.