Abstract：
BACKGROUND: the study of the possible determinants of the rise and fall of infections can be of great relevance, as was experienced during the COVID-19 pandemic. One of the methods to understand whether determinants are simultaneous or develop through contiguity between different areas is the study of the diagnostic replication index RDt among regions.
OBJECTIVE: to introduce the analysis of RDt variability and the subsequent application of a recently introduced functional clustering method as highly useful procedures for recognizing the presence of clusters with similar trends in epidemic curves.
METHODS: within the considered period, trends in regional RDt are analyzed in detail over four different time intervals.
METHODS: to exemplify this methodology, the study of variability in the period from the end of 2021 to the beginning of 2022 may be of interest.
METHODS: the variability in the regional RDt indices is assessed by means of the correlation coefficient weighted with respect to the populations of the individual regions. The clustering procedure is applied to the time series of absolute RDt values.
RESULTS: it emerges that the periods of increasing variability in the RDt correspond to the initial growth or decrease in the number of infections, while functional clustering identifies macro-areas in which the epidemic curves have had similar trends. What caused contagions to increase seems to relate to a factor that is not specific to certain areas, with the contribution in some cases of a contagion dynamic between adjacent areas.
CONCLUSIONS: the variability in the trend of regional diagnostic replication indices, which are calculated with only a few days delay, is a further indicator for the early detection of major changes in the trend of epidemic curves. The clustering of epidemic index curves may be useful to determine whether determinants act simultaneously or by contiguity between adjacent areas.
OBJECTIVE: to introduce the analysis of RDt variability and the subsequent application of a recently introduced functional clustering method as highly useful procedures for recognizing the presence of clusters with similar trends in epidemic curves.
METHODS: within the considered period, trends in regional RDt are analyzed in detail over four different time intervals.
METHODS: to exemplify this methodology, the study of variability in the period from the end of 2021 to the beginning of 2022 may be of interest.
METHODS: the variability in the regional RDt indices is assessed by means of the correlation coefficient weighted with respect to the populations of the individual regions. The clustering procedure is applied to the time series of absolute RDt values.
RESULTS: it emerges that the periods of increasing variability in the RDt correspond to the initial growth or decrease in the number of infections, while functional clustering identifies macro-areas in which the epidemic curves have had similar trends. What caused contagions to increase seems to relate to a factor that is not specific to certain areas, with the contribution in some cases of a contagion dynamic between adjacent areas.
CONCLUSIONS: the variability in the trend of regional diagnostic replication indices, which are calculated with only a few days delay, is a further indicator for the early detection of major changes in the trend of epidemic curves. The clustering of epidemic index curves may be useful to determine whether determinants act simultaneously or by contiguity between adjacent areas.
摘要：
背景：对感染上升和下降的可能决定因素的研究可能具有重要意义，就像在COVID-19大流行期间所经历的那样。了解决定因素是同时发生还是通过不同区域之间的连续性发展的方法之一是研究区域之间的诊断复制指数RDt。
目的：介绍RDt变异性的分析以及最近引入的功能聚类方法的后续应用，作为识别流行曲线中具有相似趋势的聚类的存在的非常有用的程序。
方法：在考虑的时期内，在四个不同的时间间隔内详细分析了区域RDt的趋势。
方法：举例说明此方法，2021年底至2022年初期间的变异性研究可能会引起人们的兴趣。
方法：区域RDt指数的变异性是通过相对于各个区域的种群加权的相关系数来评估的。聚类过程应用于绝对RDt值的时间序列。
结果：出现了RDt变异性增加的时期对应于感染数量的初始增长或减少，而功能聚类确定了流行曲线具有相似趋势的宏观区域。导致传染病增加的原因似乎与某些地区并非特定的因素有关，在某些情况下，邻近地区之间的传染动力有所贡献。
结论：区域诊断复制指数趋势的变异性，只需延迟几天计算，是早期发现流行曲线趋势重大变化的进一步指标。流行病指数曲线的聚类可用于确定决定因素是同时起作用还是通过相邻区域之间的邻接起作用。
目的：介绍RDt变异性的分析以及最近引入的功能聚类方法的后续应用，作为识别流行曲线中具有相似趋势的聚类的存在的非常有用的程序。
方法：在考虑的时期内，在四个不同的时间间隔内详细分析了区域RDt的趋势。
方法：举例说明此方法，2021年底至2022年初期间的变异性研究可能会引起人们的兴趣。
方法：区域RDt指数的变异性是通过相对于各个区域的种群加权的相关系数来评估的。聚类过程应用于绝对RDt值的时间序列。
结果：出现了RDt变异性增加的时期对应于感染数量的初始增长或减少，而功能聚类确定了流行曲线具有相似趋势的宏观区域。导致传染病增加的原因似乎与某些地区并非特定的因素有关，在某些情况下，邻近地区之间的传染动力有所贡献。
结论：区域诊断复制指数趋势的变异性，只需延迟几天计算，是早期发现流行曲线趋势重大变化的进一步指标。流行病指数曲线的聚类可用于确定决定因素是同时起作用还是通过相邻区域之间的邻接起作用。
