LAUSR

This is a re-analysis of the outbreak in South Korea was performed, and we argue that there was likely more than one structural break in the local SARS-CoV-2 transmission To explore and examine the structural breaks in the changing patterns of Rt, generalized regression models with segmentation are employed to fit the time index t (independent variable) to Rt (dependent variable) with a Poission likelihood framework The structural break is (mainly) determined by the knot parameters in the segmented regression The number of knot parameters indicates the counts of the occurrence of the structural breaks in COVID-19 transmissibility, and the value of the knot determines when the structural break occurs (on the timeline) The numbers and values of the knot parameters are selected according to fitting performance in terms of the AIC It was found that the model with two knots (AIC = -151) outperformed the model with only one knot (AIC = -34) Additionally, the models with three and four knots had AIC of -162 and -166, respectively, both of which improved the fitting performance In this study, with a continuous modelling scheme in Rt, there may be more than one structural break in the changing patterns of COVID-19 transmissibility, which appears to be more statistically reasonable