Wu Hong-Dar Isaac, Chao Day-Yu
Department of Applied Mathematics and Institute of Statistics, National Chung Hsing University, Taichung, Taiwan.
Graduate Institute of Microbiology and Public Health, College of Veterinary Medicine, National Chung Hsing University, Taichung, 402, Taiwan.
Sci Rep. 2021 Nov 19;11(1):22553. doi: 10.1038/s41598-021-01207-4.
The development of visual tools for the timely identification of spatio-temporal clusters will assist in implementing control measures to prevent further damage. From January 2015 to June 2020, a total number of 1463 avian influenza outbreak farms were detected in Taiwan and further confirmed to be affected by highly pathogenic avian influenza subtype H5Nx. In this study, we adopted two common concepts of spatio-temporal clustering methods, the Knox test and scan statistics, with visual tools to explore the dynamic changes of clustering patterns. Since most (68.6%) of the outbreak farms were detected in 2015, only the data from 2015 was used in this study. The first two-stage algorithm performs the Knox test, which established a threshold of 7 days and identified 11 major clusters in the six counties of southwestern Taiwan, followed by the standard deviational ellipse (SDE) method implemented on each cluster to reveal the transmission direction. The second algorithm applies scan likelihood ratio statistics followed by AGC index to visualize the dynamic changes of the local aggregation pattern of disease clusters at the regional level. Compared to the one-stage aggregation approach, Knox-based and AGC mapping were more sensitive in small-scale spatio-temporal clustering.
开发用于及时识别时空聚集性的可视化工具将有助于实施控制措施，以防止进一步的损害。2015年1月至2020年6月，台湾地区共检测到1463个禽流感疫情发生场，并进一步确认受高致病性禽流感H5Nx亚型影响。在本研究中，我们采用了时空聚集性方法的两个常见概念，即诺克斯检验和扫描统计，并结合可视化工具来探索聚集模式的动态变化。由于大部分（68.6%）疫情发生场是在2015年检测到的，因此本研究仅使用了2015年的数据。第一个两阶段算法执行诺克斯检验，该检验设定了7天的阈值，并在台湾西南部六个县识别出11个主要聚集区，随后对每个聚集区实施标准偏离椭圆（SDE）方法以揭示传播方向。第二个算法应用扫描似然比统计，随后采用AGC指数来可视化区域层面疾病聚集区局部聚集模式的动态变化。与单阶段聚集方法相比，基于诺克斯检验和AGC映射在小规模时空聚集中更敏感。
