Exploring a COVID-19 Endemic Scenario: High-Resolution Agent-Based Modeling of Multiple Variants

Area of Science:

• Epidemiology
• Computational Biology
• Public Health

Background:

• The COVID-19 pandemic is complicated by the emergence of new, more infectious, and vaccine-resistant variants.
• Infrastructural challenges like personnel and test kit shortages worsen infection surges.
• Existing computational models struggle to simulate the simultaneous spread of multiple SARS-CoV-2 variants.

Purpose of the Study:

• To establish a high-resolution computational framework for modeling the simultaneous spread of two COVID-19 variants.
• To assess the impact of new variants on infection dynamics within a representative U.S. town.
• To evaluate containment strategies for emerging SARS-CoV-2 strains.

Main Methods:

• Development of a detailed database for a representative U.S. town.
• Implementation of a high-resolution agent-based model (ABM).
• Utilizing the Omicron variant as the base for modeling, with flexibility for incorporating new variants.

Main Results:

• The spread of new COVID-19 variants can be contained with highly efficacious tests and minimal loss of vaccine protection.
• The Omicron variant's aggressiveness and waning vaccine immunity suggest an endemic phase.
• Coexistence of multiple SARS-CoV-2 variants is likely in the ongoing endemic phase.

Conclusions:

• Effective testing and maintained vaccine efficacy are crucial for controlling new COVID-19 variants.
• The current trajectory indicates an endemic future for COVID-19, characterized by persistent infections and variant co-circulation.
• Agent-based modeling provides a valuable tool for understanding and predicting the impact of emerging infectious disease variants.