TRACE-Omicron: Policy Counterfactuals to Inform Mitigation of COVID-19 Spread in the United States

TL;DR

This study uses a large-scale agent-based model to evaluate how different policy interventions could have mitigated the Omicron COVID-19 wave in the US, emphasizing early, combined actions over extreme measures.

Contribution

Introduces a comprehensive agent-based simulation of US population behaviors and policies to assess COVID-19 mitigation strategies during the Omicron wave.

Findings

Combined interventions could have significantly reduced cases and hospitalizations.

Early and decisive policy actions are crucial for effective mitigation.

Extreme measures like widespread closures are less necessary if other interventions are timely.

Abstract

The Omicron wave was the largest wave of COVID-19 pandemic to date, more than doubling any other in terms of cases and hospitalizations in the United States. In this paper, we present a large-scale agent-based model of policy interventions that could have been implemented to mitigate the Omicron wave. Our model takes into account the behaviors of individuals and their interactions with one another within a nationally representative population, as well as the efficacy of various interventions such as social distancing, mask wearing, testing, tracing, and vaccination. We use the model to simulate the impact of different policy scenarios and evaluate their potential effectiveness in controlling the spread of the virus. Our results suggest the Omicron wave could have been substantially curtailed via a combination of interventions comparable in effectiveness to extreme and unpopular singular measures such as widespread closure of schools and workplaces, and highlight the importance of early and decisive action.