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<p>During the COVID-19 pandemic, fluctuations in daily cases and severe infections posed substantial challenges to healthcare capacity, particularly in intensive care units (ICUs). This period underscored the need for data-driven decision-making systems to efficiently manage the limited healthcare resources. Therefore, to mitigate ICU overload during pandemic surges, we evaluated strategies to tighten non-pharmaceutical interventions (NPI) and expand ICU-bed capacity. We developed an age-structured transmission model incorporating ICU occupancy rate (IOR)-based activation thresholds, decision-to-action delays, and combined interventions targeting both demand (NPI intensity) and supply (capacity expansion). Our results indicated that during a seven-day delay in implementing control measures, maintaining the IOR below a high-occupancy benchmark of 0.8 requires escalation to at least NPI Level 3 when activation occurs before the IOR exceeds 0.5. When NPIs are strengthened with ICU-bed expansion, maintaining an IOR below 0.8 becomes feasible under milder NPIs (e.g., NPI Level 1), provided activation remains timely (before the IOR exceeds 0.6) and capacity increases by at least 10%. Escalation to NPI Level 3 further reduces the required expansion and yields control that is more stable across conditions. These findings provide a data-driven basis for ICU resource planning and policy design during health crises. The framework can inform the assessment of adaptive intervention strategies and support evidence-based healthcare resource planning in future health emergencies beyond the COVID-19 context.</p>

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Modeling-based strategies for ICU-bed management during the COVID-19 pandemic

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月23日

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  • Geunsoo Jang,
  • Sumin Park,
  • Yongin Choi,
  • Sung June Choi,
  • Hyojung Lee

摘要

During the COVID-19 pandemic, fluctuations in daily cases and severe infections posed substantial challenges to healthcare capacity, particularly in intensive care units (ICUs). This period underscored the need for data-driven decision-making systems to efficiently manage the limited healthcare resources. Therefore, to mitigate ICU overload during pandemic surges, we evaluated strategies to tighten non-pharmaceutical interventions (NPI) and expand ICU-bed capacity. We developed an age-structured transmission model incorporating ICU occupancy rate (IOR)-based activation thresholds, decision-to-action delays, and combined interventions targeting both demand (NPI intensity) and supply (capacity expansion). Our results indicated that during a seven-day delay in implementing control measures, maintaining the IOR below a high-occupancy benchmark of 0.8 requires escalation to at least NPI Level 3 when activation occurs before the IOR exceeds 0.5. When NPIs are strengthened with ICU-bed expansion, maintaining an IOR below 0.8 becomes feasible under milder NPIs (e.g., NPI Level 1), provided activation remains timely (before the IOR exceeds 0.6) and capacity increases by at least 10%. Escalation to NPI Level 3 further reduces the required expansion and yields control that is more stable across conditions. These findings provide a data-driven basis for ICU resource planning and policy design during health crises. The framework can inform the assessment of adaptive intervention strategies and support evidence-based healthcare resource planning in future health emergencies beyond the COVID-19 context.