Modeling epidemic and economic trade-offs under alternative COVID-19 isolation exit scenarios in South Korea

Scientific evidence should guide adjustments to isolation policies as part of coronavirus disease 2019 (COVID-19) exit strategies. However, such evidence was limited when these strategies were implemented. This study retrospectively evaluated the relaxation of isolation policies in South Korea, aiming to generate evidence to support policy decisions in future pandemic scenarios.

We developed an age-stratified deterministic vaccination-extended Susceptible-Exposed-Infectious-Recovered model to simulate counterfactual scenarios related to isolation policy changes during the Omicron wave. Two phases were assessed: partial relaxation of isolation in 2022 and complete lifting of isolation mandates in 2023. Model outputs included confirmed cases and wage loss. Modified cost-effectiveness ratios were calculated as economic savings per additional infection.

Moderate shortening of isolation from 10 to 7 days before the epidemic peak was associated with relatively favorable trade-offs between additional infections and wage loss reduction. In contrast, a more aggressive approach, reducing isolation to 5 days, resulted in a substantially higher number of confirmed cases with only marginal additional economic benefit. Delaying full lifting until July 2023, 1 month after the actual policy change, led to smaller and later epidemic peaks and greater economic gains.

Our findings suggested that both the timing and extent of isolation policy relaxation significantly influence epidemic dynamics and economic-epidemiologic trade-offs. Scenario-based modeling offers a valuable tool for anticipating trade-offs and informing policy decisions. Strengthening real-time surveillance and predictive modeling infrastructure is critical for an adaptive and balanced pandemic response.