Department of Geography, University of Connecticut, Storrs, CT, 06269, USA.
Institute for Collaboration on Health, Intervention, and Policy (InCHIP), University of Connecticut, Storrs, CT, 06269, USA.
BMC Med Res Methodol. 2022 Aug 30;22(1):233. doi: 10.1186/s12874-022-01714-6.
One critical variable in the time series analysis is the change point, which is the point where an abrupt change occurs in chronologically ordered observations. Existing parametric models for change point detection, such as the linear regression model and the Bayesian model, require that observations are normally distributed and that the trend line cannot have extreme variability. To overcome the limitations of the parametric model, we apply a nonparametric method, the Mann-Kendall-Sneyers (MKS) test, to change point detection for the state-level COVID-19 case time series data of the United States in the early outbreak of the pandemic.
The MKS test is implemented for change point detection. The forward sequence and the backward sequence are calculated based on the new weekly cases between March 22, 2020 and January 31, 2021 for each of the 50 states. Points of intersection between the two sequences falling within the 95% confidence intervals are identified as the change points. The results are compared with two other change point detection methods, the pruned exact linear time (PELT) method and the regression-based method. Also, an open-access tool by Microsoft Excel is developed to facilitate the model implementation.
By applying the MKS test to COVID-19 cases in the United States, we have identified that 30 states (60.0%) have at least one change point within the 95% confidence intervals. Of these states, 26 states have one change point, 4 states (i.e., LA, OH, VA, and WA) have two change points, and one state (GA) has three change points. Additionally, most downward changes appear in the Northeastern states (e.g., CT, MA, NJ, NY) at the first development stage (March 23 through May 31, 2020); most upward changes appear in the Western states (e.g., AZ, CA, CO, NM, WA, WY) and the Midwestern states (e.g., IL, IN, MI, MN, OH, WI) at the third development stage (November 19, 2020 through January 31, 2021).
This study is among the first to explore the potential of the MKS test applied for change point detection of COVID-19 cases. The MKS test is characterized by several advantages, including high computational efficiency, easy implementation, the ability to identify the change of direction, and no assumption for data distribution. However, due to its conservative nature in change point detection and moderate agreement with other methods, we recommend using the MKS test primarily for initial pattern identification and data pruning, especially in large data. With modification, the method can be further applied to other health data, such as injuries, disabilities, and mortalities.
时间序列分析中的一个关键变量是变化点，即时间顺序观测值中突然发生变化的点。现有的变化点检测参数模型，如线性回归模型和贝叶斯模型，要求观测值呈正态分布，趋势线不能有极端变化。为了克服参数模型的局限性，我们应用非参数方法，即曼恩-肯德尔-斯尼耶斯（MKS）检验，来检测美国大流行早期的州级 COVID-19 病例时间序列数据的变化点。
应用 MKS 检验进行变化点检测。对于 2020 年 3 月 22 日至 2021 年 1 月 31 日期间每个州的新每周病例数，计算前向序列和后向序列。两个序列之间交点落在 95%置信区间内的点被确定为变化点。结果与另外两种变化点检测方法（修剪精确线性时间（PELT）方法和基于回归的方法）进行比较。此外，还开发了一个 Microsoft Excel 的开放访问工具来方便模型的实施。
通过将 MKS 检验应用于美国的 COVID-19 病例，我们发现 30 个州（60.0%）在 95%置信区间内至少有一个变化点。在这些州中，有 26 个州有一个变化点，有 4 个州（即 LA、OH、VA 和 WA）有两个变化点，有一个州（GA）有三个变化点。此外，大多数下降变化出现在东北部各州（如 CT、MA、NJ、NY）的第一发展阶段（2020 年 3 月 23 日至 5 月 31 日）；大多数上升变化出现在西部各州（如 AZ、CA、CO、NM、WA、WY）和中西部各州（如 IL、IN、MI、MN、OH、WI）的第三发展阶段（2020 年 11 月 19 日至 2021 年 1 月 31 日）。
本研究是首次探索 MKS 检验应用于 COVID-19 病例变化点检测的潜力。MKS 检验具有几个优点，包括计算效率高、易于实现、能够识别方向变化以及对数据分布没有假设。然而，由于其在变化点检测中的保守性以及与其他方法的中等一致性，我们建议主要将 MKS 检验用于初始模式识别和数据修剪，尤其是在大数据中。经过修改，该方法可以进一步应用于其他健康数据，如伤害、残疾和死亡。
