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Accepted for/Published in: JMIR Medical Informatics

Date Submitted: Jan 24, 2021
Date Accepted: Apr 14, 2021
Date Submitted to PubMed: Apr 19, 2021

The final, peer-reviewed published version of this preprint can be found here:

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

Li J, Giabbanelli P

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

JMIR Med Inform 2021;9(4):e27419

DOI: 10.2196/27419

PMID: 33872188

PMCID: 8086790

Returning to a normal life via COVID-19 vaccines in the USA: a large-scale agent-based simulation study

  • Junjiang Li; 
  • Philippe Giabbanelli

ABSTRACT

Background:

In 2020, COVID-19 has claimed more than 300,000 deaths in the US alone. While non-pharmaceutical interventions were implemented by federal and state governments in the USA, these efforts have failed to contain the virus. Following the FDA approval of two COVID-19 vaccines, however, the hope for the return to normalcy is renewed. This hope rests on an unprecedented nation-wide 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 nation-wide 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 non-pharmaceutical interventions and vaccines, through six scenarios that capture a range of possible impact from non-pharmaceutical interventions.

Methods:

We use large-scale cloud-based agent-based simulations by implementing the vaccination campaign using Covasim, an open-source ABM for COVID-19 that has been used in several peer-reviewed studies and accounts for individual heterogeneity as well as 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 six non-pharmaceutical 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 is fitted to a logistic growth model, and the carrying capacities and the growth rates are recorded.

Results:

For both vaccination plans and all non-pharmaceutical 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 at 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 non-pharmaceutical 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. Non-pharmaceutical 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.


 Citation

Please cite as:

Li J, Giabbanelli P

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

JMIR Med Inform 2021;9(4):e27419

DOI: 10.2196/27419

PMID: 33872188

PMCID: 8086790

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© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.

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