Department of Applied Mathematics, Kyung Hee University, Yongin, Republic of Korea.
Department of Mathematics, Texas State University, San Marcos, TX, USA.
Sci Rep. 2021 Dec 28;11(1):24470. doi: 10.1038/s41598-021-03487-2.
A novel severe acute respiratory syndrome coronavirus 2 emerged in December 2019, and it took only a few months for WHO to declare COVID-19 as a pandemic in March 2020. It is very challenging to discover complex spatial-temporal transmission mechanisms. However, it is crucial to capture essential features of regional-temporal patterns of COVID-19 to implement prompt and effective prevention or mitigation interventions. In this work, we develop a novel framework of compatible window-wise dynamic mode decomposition (CwDMD) for nonlinear infectious disease dynamics. The compatible window is a selected representative subdomain of time series data, in which compatibility between spatial and temporal resolutions is established so that DMD can provide meaningful data analysis. A total of four compatible windows have been selected from COVID-19 time-series data from January 20, 2020, to May 10, 2021, in South Korea. The spatiotemporal patterns of these four windows are then analyzed. Several hot and cold spots were identified, their spatial-temporal relationships, and some hidden regional patterns were discovered. Our analysis reveals that the first wave was contained in the Daegu and Gyeongbuk areas, but it spread rapidly to the whole of South Korea after the second wave. Later on, the spatial distribution is seen to become more homogeneous after the third wave. Our analysis also identifies that some patterns are not related to regional relevance. These findings have then been analyzed and associated with the inter-regional and local characteristics of South Korea. Thus, the present study is expected to provide public health officials helpful insights for future regional-temporal specific mitigation plans.
一种新型严重急性呼吸系统综合症冠状病毒 2 于 2019 年 12 月出现，仅用了几个月时间，世界卫生组织就于 2020 年 3 月宣布 COVID-19 为大流行。发现复杂的时空传播机制极具挑战性。然而，捕捉 COVID-19 的区域性时空模式的基本特征对于实施及时有效的预防或缓解干预措施至关重要。在这项工作中，我们开发了一种新的兼容窗口动态模态分解（CwDMD）框架，用于非线性传染病动力学。兼容窗口是时间序列数据的一个选定代表性子域，其中建立了空间和时间分辨率之间的兼容性，以便 DMD 可以提供有意义的数据分析。总共从 2020 年 1 月 20 日至 2021 年 5 月 10 日韩国的 COVID-19 时间序列数据中选择了四个兼容窗口。然后分析了这四个窗口的时空模式。确定了一些热点和冷点，发现了它们的时空关系和一些隐藏的区域模式。我们的分析表明，第一波疫情局限于大邱和庆尚北道地区，但第二波疫情后迅速蔓延到韩国全境。之后，第三波疫情后，空间分布变得更加均匀。我们的分析还发现，一些模式与区域相关性无关。然后对这些发现进行了分析，并与韩国的区域和地方特征相关联。因此，本研究有望为公共卫生官员提供有价值的见解，以制定未来针对特定区域和时间的缓解计划。
