Korea Institute of Public Finance, 336, Sicheong-daero, Sejong 30147, Republic of Korea.
Public Health. 2021 Dec;201:89-97. doi: 10.1016/j.puhe.2021.10.002. Epub 2021 Oct 18.
Observing cumulative and new daily confirmed cases of COVID-19, disease control authorities respond to a surge in cases with social distancing measures or economic lockdown. The question in this article is whether we can gather more useful information from a readily available time series data set of day-to-day changes in confirmed cases of COVID-19.
Time-series data analysis was done using a hidden Markov model.
Day-to-day differences in confirmed cases of COVID-19 in Korea from February 19, 2020, to July 13, 2021, were modeled via a hidden Markov model. The results from the model were compared with the effective reproduction number and the Korean government's response.
The model reports that Korea was in an epidemic phase from August 2020 and from mid-November 2020, the second and third epidemic waves. The government's response, represented by the Government Response Stringency Index, was not timely during the epidemic phases. The results from the model may also be more helpful to detect the onset of the epidemic phase of an infectious disease than the effective reproduction number.
The model can reveal a hidden epidemic phase and help disease control authorities to respond more promptly and effectively.
观察 COVID-19 的累计和每日新增确诊病例，疾病控制当局通过采取社交距离措施或经济封锁来应对病例激增。本文的问题是，我们是否可以从现有的 COVID-19 确诊病例日变化的时间序列数据集收集到更多有用的信息。
采用隐马尔可夫模型进行时间序列数据分析。
通过隐马尔可夫模型对 2020 年 2 月 19 日至 2021 年 7 月 13 日韩国的 COVID-19 确诊病例的日差异进行建模。将模型的结果与有效繁殖数和韩国政府的应对措施进行比较。
模型报告称，韩国从 2020 年 8 月和 2020 年 11 月中旬进入流行阶段，即第二波和第三波疫情。代表政府应对措施的政府应对强度指数在流行阶段并不及时。与有效繁殖数相比，模型的结果可能更有助于发现传染病流行阶段的开始。
该模型可以揭示隐藏的流行阶段，并帮助疾病控制当局更及时、更有效地做出反应。
