Dynamic interventions to control COVID-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide...

Area of Science:

• Epidemiology
• Public Health Policy
• Mathematical Modeling

Background:

• Non-pharmacological interventions (NPIs) have been crucial for controlling COVID-19 but pose economic challenges.
• Lifting NPIs risks disease resurgence, necessitating adaptable control strategies.
• Uncertainty exists regarding optimal intermittent NPIs, especially in resource-limited settings.

Purpose of the Study:

• To evaluate the effectiveness of dynamic NPIs in controlling COVID-19 trajectories.
• To compare mitigation and suppression strategies with intermittent relaxation periods.
• To assess impacts on intensive care unit (ICU) admissions and deaths across diverse countries.

Main Methods:

• A multivariate prediction model simulated COVID-19 outbreak trajectories in 16 countries.
• Scenarios included no intervention, dynamic mitigation (R=0.8), and dynamic suppression (R=0.5) with 50-day cycles and 30-day relaxations.
• Impacts on ICU admissions and deaths were modeled over 18 months.

Main Results:

• Dynamic mitigation cycles (50-day on, 30-day off) reduced transmission but not ICU hospitalizations.
• Dynamic suppression cycles (50-day on, 30-day off) maintained ICU demand below national capacities.
• Significant reductions in infections and deaths were estimated, especially in resource-poor countries.

Conclusions:

• Intermittent NPIs, specifically dynamic suppression, can effectively control COVID-19 by reducing the effective reproduction number below 1.
• This strategy is a pragmatic option for preventing critical care overload and economic hardship, particularly in low-income countries.
• Dynamic suppression provides time for developing preventive and clinical measures while mitigating economic impact.