Returning to a Normal Life via COVID-19 Vaccines in the United States: A Large-scale Agent-Based Simulation Study

Abstract

Background: In 2020, COVID-19 has claimed more than 300,000 deaths in the United States alone. Although nonpharmaceutical interventions were implemented by federal and state governments in the United States, these efforts have failed to contain the virus. Following the Food and Drug Administration's approval of two COVID-19 vaccines, however, the hope for the return to normalcy has been renewed. This hope rests on an unprecedented nationwide vaccine campaign, which faces many logistical challenges and is also contingent on several factors whose values are currently unknown.

Objective: We study the effectiveness of a nationwide vaccine campaign in response to different vaccine efficacies, the willingness of the population to be vaccinated, and the daily vaccine capacity under two different federal plans. To characterize the possible outcomes most accurately, we also account for the interactions between nonpharmaceutical interventions and vaccines through 6 scenarios that capture a range of possible impacts from nonpharmaceutical interventions.

Methods: We used large-scale, cloud-based, agent-based simulations by implementing the vaccination campaign using COVASIM, an open-source agent-based model for COVID-19 that has been used in several peer-reviewed studies and accounts for individual heterogeneity and a multiplicity of contact networks. Several modifications to the parameters and simulation logic were made to better align the model with current evidence. We chose 6 nonpharmaceutical intervention scenarios and applied the vaccination intervention following both the plan proposed by Operation Warp Speed (former Trump administration) and the plan of one million vaccines per day, proposed by the Biden administration. We accounted for unknowns in vaccine efficacies and levels of population compliance by varying both parameters. For each experiment, the cumulative infection growth was fitted to a logistic growth model, and the carrying capacities and the growth rates were recorded.

Results: For both vaccination plans and all nonpharmaceutical intervention scenarios, the presence of the vaccine intervention considerably lowers the total number of infections when life returns to normal, even when the population compliance to vaccines is as low as 20%. We noted an unintended consequence; given the vaccine availability estimates under both federal plans and the focus on vaccinating individuals by age categories, a significant reduction in nonpharmaceutical interventions results in a counterintuitive situation in which higher vaccine compliance then leads to more total infections.

Conclusions: Although potent, vaccines alone cannot effectively end the pandemic given the current availability estimates and the adopted vaccination strategy. Nonpharmaceutical interventions need to continue and be enforced to ensure high compliance so that the rate of immunity established by vaccination outpaces that induced by infections.

Keywords: COVID-19; United States; agent-based; agent-based model; capacity; cloud-based simulations; effective; impact; interaction; intervention; large-scale simulations; model; outcome; plan; scenario; simulation; strategy; vaccine; willingness.

Figure 1

Overview of our modified COVASIM model containing the state diagram and specification of all transitions, including key procedures for vaccination and infection.

Figure 2

Number of new infections during the simulation (ie, cumulative cases) under five scenarios (each based on a combination of interventions), which were selected for their ability to represent different trends in the number of cases over time, without a vaccine.

Figure 3

Comparison of changes in cumulative infections between a COVASIM simulation and reality from September 3, 2020, to the end of 2020. The simulation included a reduction on work and school contacts (set to 95% of their capacity), 600,000 daily and highly sensitive tests, quarantining upon testing, immediate tracing to identify all contacts, and a presumptive approach.

Figure 4

Distributions of the average goodness of fit R² for each vaccination plan, demonstrating the validity of fitting logistic growth models from t≥20.

Figure 5

Number of new infections during the simulation (ie, cumulative cases) under Operation Warp Speed with vaccine compliance of 0.6, vaccine efficacy of 0.99, scenario 1 for nonpharmaceutical interventions (“controlled” case: blue), and scenario 6 consisting of no interventions (“uncontrolled” case: orange).

Figure 6

Our simulation management architecture to leverage parallelism on Microsoft Azure.

Figure 7

Adjusted growth rate (number of infected individuals on the worst day) as functions of vaccine compliance and efficacy under the Biden vaccination plan.

Figure 8

Adjusted growth rate (number of infected individuals on the worst day) as functions of vaccine compliance and efficacy under the Trump vaccination plan.