Policy Review and Modeling Analysis of Mitigation Measures for Coronavirus Disease Epidemic Control, Health System, and Disease Burden, South Korea

Abstract

We reviewed the timeline of key policies for control of the coronavirus disease epidemic and determined their impact on the epidemic and hospital burden in South Korea. Using a discrete stochastic transmission model, we estimated that multilevel policies, including extensive testing, contact tracing, and quarantine, reduced contact rates by 90% and rapidly decreased the epidemic in Daegu and nationwide during February‒March 2020. Absence of these prompt responses could have resulted in a >10-fold increase in infections, hospitalizations, and deaths by May 15, 2020, relative to the status quo. The model suggests that reallocation of persons who have mild or asymptomatic cases to community treatment centers helped avoid overwhelming hospital capacity and enabled healthcare workers to provide care for more severely and critically ill patients in hospital beds and negative-pressure intensive care units. As small outbreaks continue to occur, contact tracing and maintenance of hospital capacity are needed.

Keywords: COVID-19; SARS-CoV-2; South Korea; coronavirus disease; coronaviruses; epidemic model; infection control; mathematical modelling; respiratory infections; severe acute respiratory syndrome coronavirus 2; viruses; zoonoses.

Figure

Estimated and confirmed numbers for coronavirus disease, South Korea, 2020. A) New daily cases; B) hospital census; C) CTC census; D) ICU census; E) cumulative deaths. Gray lines indicate observed data (5,6,9,16). Blue lines indicate estimated numbers with 35%, 50%, and 90% reductions in the basic reproduction number by February 26, February 28, and March 2, respectively, as the status quo. Additional scenarios are shown where R₀ was assumed to be reduced by 70% (scenario 1, green line) or stayed the same at 50% (scenario 2, red line) after February 28. CTC, community treatment center; ICU, intensive care unit; R₀, basic reproduction number.