Policy optimization for multi-variant COVID-19 pandemic under unequal isolation and booster shot

Area of Science:

• Epidemiology and Public Health
• Health Economics
• Computational Modeling

Background:

• COVID-19 presents ongoing global challenges with new variants and waves.
• Effective public health policies (vaccination, isolation) are vital for mitigating health and socio-economic impacts.
• Budget constraints necessitate optimized policies balancing infection control with economic and social well-being.

Purpose of the Study:

• To analyze multi-variant COVID-19 spread using a System Dynamics (SD) model.
• To evaluate the impact of vaccination strategies, booster shots, vaccine supply, and isolation rates.
• To determine optimal policies for controlling COVID-19 variants under economic constraints.

Main Methods:

• Development and application of a System Dynamics (SD) model.
• Simulation of COVID-19 spread considering Alpha, Delta, and Wuhan variants in the United States.
• Desirability-based optimization for policy parameters.

Main Results:

• Base vaccination and isolation policies are insufficient against the Delta variant.
• Prioritizing vaccines for the unvaccinated is more effective than boosting the vaccinated.
• Combined strategies of increased vaccines, enhanced isolation, and boosters show the most promise for variant suppression.
• Unequal isolation policies outperform equal isolation.

Conclusions:

• Optimized vaccination and isolation strategies are crucial for managing multi-variant COVID-19.
• Policy decisions must balance public health objectives with economic considerations.
• System Dynamics modeling provides a valuable framework for evaluating complex public health interventions.