Lessons drawn from China and South Korea for managing COVID-19 epidemic: Insights from a comparative modeling study

Abstract

We conducted a comparative study of the COVID-19 epidemic in three different settings: mainland China, the Guangdong province of China and South Korea, by formulating two disease transmission dynamics models which incorporate epidemic characteristics and setting-specific interventions, and fitting the models to multi-source data to identify initial and effective reproduction numbers and evaluate effectiveness of interventions. We estimated the initial basic reproduction number for South Korea, the Guangdong province and mainland China as 2.6 (95% confidence interval (CI): (2.5, 2.7)), 3.0 (95%CI: (2.6, 3.3)) and 3.8 (95%CI: (3.5,4.2)), respectively, given a serial interval with mean of 5 days with standard deviation of 3 days. We found that the effective reproduction number for the Guangdong province and mainland China has fallen below the threshold 1 since February 8th and 18th respectively, while the effective reproduction number for South Korea remains high until March 2nd Moreover our model-based analysis shows that the COVID-19 epidemics in South Korean is almost under control with the cumulative confirmed cases tending to be stable as of April 14th. Through sensitivity analysis, we show that a coherent and integrated approach with stringent public health interventions is the key to the success of containing the epidemic in China and especially its provinces outside its epicenter. In comparison, we find that the extremely high detection rate is the key factor determining the success in controlling the COVID-19 epidemics in South Korea. The experience of outbreak control in mainland China and South Korea should be a guiding reference for the rest of the world.

Keywords: COVID-19 epidemic; Comparative study; Mainland china and south korea; Mathematical model; Multi-source data.

Fig. 1

The datasets related to the COVID-19 epidemics, including newly reported cases, cumulative number of reported cases, cumulative number of cured cases, cumulative number of death cases, cumulative quarantined cases and cumulative suspected cases for mainland China (A–C), the Guangdong province of China (E), and South Korea (F).

Fig. 2

The illustration of the compartmental models incorporating important interventions and features of reporting systems, for mainland China (model I) including its Province of Guangdong, and for South Korea (model II).

Fig. 3

Estimated effective reproduction number $R_t$ over sliding weekly windows for the entire country of China, the Guangdong province of China, and Korea. The solid lines show the posterior means and the colored zones show the 95% confidence intervals; the horizontal dashed line indicates $R_t=1$.(For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

Model fitting results (curves marked as black) and variations in cumulative number of reported cases (A), death cases (B), quarantined cases (C) and suspected cases (D) for mainland China. (E) Variation in number of infected (asymptomatic/symptomatic) individuals with contact rate function $c\left(t\right)$. (F) Here the contact rate function is changed by varying the exponential decreasing rate $r_1$, representing the variation in intensity of control measures. $r_{10}$ denotes the estimated baseline value of $r_1$.

Fig. 5

Model fitting results (curves marked as black) and variations in cumulative number of reported cases (A), cumulative number of death cases (B), quarantined cases (C) and suspected cases (D) for mainland China. (E) Variation in number of infected (asymptomatic/symptomatic) individuals with detection rate function ${\delta }_I\left(t\right)$. (F) Variation in the effective reproduction number of China. Here the detection rate function is changed by varying the exponential decreasing rate $r_3$, representing the variation in intensity of control measures. $r_{30}$ denotes the estimated baseline value of $r_3$.

Fig. 6

(A–B) Model fitting results and the impact of the randomness of the data set including the cumulative number of tested cases and the cumulative number of reported cases on the COVID-19 epidemic in Korea. The 95% confidence intervals have been given and the mean curve is marked as black. The red cycles denote the real data. (C–D) Sensitivity analysis. The impact of the diagnose rate ${\delta }_I$ on the cumulative confirmed cases and the infected populations in South Korea. Here, ${\delta }_{I0}$ denotes the estimated baseline value of the diagnose rate.. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 7

Model fitting results (curves marked as green) and variations in cumulative number of reported cases (A), recovery cases (B), and suspected cases (C) for Guangdong province. (D) Variation in the effective reproduction number with parameter $r_1$ in contact rate function $c\left(t\right)$. Here the contact rate function is changed by varying the exponential decreasing rate $r_1$, representing the variation in intensity of control measures. $r_{10}$ denotes the estimated baseline value for parameter $r_1$.. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 8

Goodness of fit (green curves) and variations in cumulative number of reported cases (A), recovery cases (B), and suspected cases (C) for Guangdong province. (D) Variation in the effective reproduction number with parameter $r_3$ in detection rate function ${\delta }_I\left(t\right)$. Here the detection rate function is changed by varying the exponential decreasing rate $r_3$, representing the variation in intensity of control measures. $r_{30}$ denotes the estimated baseline value of $r_3$.. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)