Abstract
Objectives: 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.
Study design: Time-series data analysis was done using a hidden Markov model.
Methods: 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.
Results: 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.
Conclusions: The model can reveal a hidden epidemic phase and help disease control authorities to respond more promptly and effectively.
Keywords: COVID-19; Effective reproduction number; Epidemic phase; Government response stringency index; Hidden Markov model.
【저자키워드】 COVID-19, effective reproduction number, Epidemic phase, Government response stringency index, Hidden Markov model., 【초록키워드】 lockdown, Infectious disease, Epidemic, response, disease control, Data analysis, Reproduction number, hidden markov model, information, disease, Korean, data set, Government, confirmed case, index, confirmed cases, social distancing measures, help, cumulative, Social distancing measure, Markov, effective, the epidemic, detect, question, changes in, respond, was done, hidden, Observing, 【제목키워드】 social distancing, the epidemic, the timing,