J Korean Med Sci.  2020 Sep;35(35):e321. 10.3346/jkms.2020.35.e321.

Estimating the Effectiveness of Non-Pharmaceutical Interventions on COVID-19 Control in Korea

• Min KD
• Kang H
• Lee JY
• Jeon S
• Cho Si

• KMID: 2506088
• DOI: http://doi.org/10.3346/jkms.2020.35.e321

Abstract

Background
The coronavirus disease 2019 (COVID-19) pandemic has posed significant global public health challenges and created a substantial economic burden. Korea has experienced an extensive outbreak, which was linked to a religion-related super-spreading event. However, the implementation of various non-pharmaceutical interventions (NPIs), including social distancing, spring semester postponing, and extensive testing and contact tracing controlled the epidemic. Herein, we estimated the effectiveness of each NPI using a simulation model.
Methods
A compartment model with a susceptible-exposed-infectious-quarantinedhospitalized structure was employed. Using the Monte-Carlo-Markov-Chain algorithm with Gibbs' sampling method, we estimated the time-varying effective contact rate to calibrate the model with the reported daily new confirmed cases from February 12th to March 31st (7 weeks). Moreover, we conducted scenario analyses by adjusting the parameters to estimate the effectiveness of NPI.
Results
Relaxed social distancing among adults would have increased the number of cases 27.4-fold until the end of March. Spring semester non-postponement would have increased the number of cases 1.7-fold among individuals aged 0–19, while lower quarantine and detection rates would have increased the number of cases 1.4-fold.
Conclusion
Among the three NPI measures, social distancing in adults showed the highest effectiveness. The substantial effect of social distancing should be considered when preparing for the 2nd wave of COVID-19.

Keyword

COVID-19; Mathematical Model; Non-pharmaceutical Interventions; Gibbs' Sampling

Figure

• 

  Fig. 1 Schematic diagram of the compartment model used in this study.The population was categorized into five states: susceptible (S), exposed (E), infectious (I), quarantined (Q), and hospitalized (H). The population was also stratified according to age: aged 0–19 (subscript “c”) and aged 20+ (subscript “a”). Four types of parameters were used to determine the transition rates between the different states: the rate at which exposed individuals become infective (parameter θ), the detection (or isolation) rates of infectious individuals (parameter γ), quarantine probability (parameter ν), and the force of infection (parameter λ), which was time-varying and dependent on the number of infectious individuals and their effective contact rates.

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  Fig. 2 Calibration of the simulation model using the number of reported confirmed cases.(A, B) Calibration of the simulation model (red) using the daily reported new cases (black) in individuals aged 0–19 (A) and 20+ (B). (C, D) Pearson correlation analysis showing a strong positive correlation between the simulated number and the number of reported new cases. (E, F) The time-varying effective contact rates were estimated using the Monte-Carlo-Markov-Chain algorithm.

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  Fig. 3 Effectiveness of social distancing as determined by scenario analyses.Note: The effectiveness of social distancing among adults was estimated by increasing the effective contact rate in individuals aged 20+. In the severe-case scenario, the effective contact rate in weeks 3–7 was assumed to be 0.497 (95% credible interval = 0.410–0.578), which was two times higher than the maximum estimated effective contact rate in weeks 3–7. In the mild-case scenario, the effective contact rate in weeks 3–7 was assumed to be 0.249 (95% credible interval = 0.205–0.289), which was equal to the maximum effective contact rate estimated for weeks 3–7.

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  Fig. 4 Effectiveness of spring semester postponement as determined by scenario analyses.The effectiveness of the spring semester postponement was estimated by increasing the effective contact rate among individuals aged 0–19. In the severe-case scenario, the highest effective contact rate between March 2nd and March 31st was 0.170 (95% credible interval = 0.151–0.190), which was 2.39 times higher than the effective contact rate estimated for February. In the mild-case scenario, the estimated effective contact rate from March 2nd to March 31st was 0.124 (95% credible interval = 0.120–0.127), which was 1.75 times higher than the effective contact rate estimated for February.

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  Fig. 5 Effectiveness of extensive diagnostic testing and contact tracing as determined by scenario analyses.(A-D) The effectiveness of extensive diagnostic testing and contact tracing was estimated by decreasing the quarantine probability and detection rate. In the scenario analysis, the quarantine probability was reduced to 2%, which was half the status quo (4%). Additionally, the detection rate was reduced to 1/9.09, reflecting a longer infectious period (9.09 days) than the status quo (5.8 days). (E) Contour plot illustrating the variations in the additional cumulative cases by the end of March.

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