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Research Article | Open Access

Optimization of vaccination for COVID-19 in the midst of a pandemic

Department of Industrial Engineering, Clemson University, Clemson, SC, USA
Center for Computational and Integrative Biology, Rutgers Camden, Camden NJ, USA
Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125, USA
Sorbonne Université, CNRS, Université Paris Cité, Inria, Laboratoire Jacques-Louis Lions (LJLL), F-75005 Paris, France
Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, TN, USA
School of Civil and Environmental Engineering, Cornell University, Ithaca, NY, USA
Joseph and Loretta Lopez chair professor of Mathematics, Center for Computational and Integrative Biology, Rutgers Camden, Camden NJ, USA
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Abstract

During the Covid-19 pandemic a key role is played by vaccination to combat the virus. There are many possible policies for prioritizing vaccines, and different criteria for optimization: minimize death, time to herd immunity, functioning of the health system. Using an age-structured population compartmental finite-dimensional optimal control model, our results suggest that the eldest to youngest vaccination policy is optimal to minimize deaths. Our model includes the possible infection of vaccinated populations. We apply our model to real-life data from the US Census for New Jersey and Florida, which have a significantly different population structure. We also provide various estimates of the number of lives saved by optimizing the vaccine schedule and compared to no vaccination.

CLC number: Primary: 58F15, 58F17; Secondary: 53C35

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Networks and Heterogeneous Media
Pages 443-466

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Cite this article:
Luo Q, Weightman R, McQuade ST, et al. Optimization of vaccination for COVID-19 in the midst of a pandemic. Networks and Heterogeneous Media, 2022, 17(3): 443-466. https://doi.org/10.3934/nhm.2022016

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Received: 01 September 2021
Revised: 01 January 2022
Published: 15 June 2022
©2022 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)