LAUSR

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 paper is whether we can gather more useful information from a readily available time-series dataset of day-to-day changes in confirmed cases of COVID-19. Study Design Time-series data analysis via 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. 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.