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

Qi Luo; Ryan Weightman; Sean T. McQuade; Mateo Díaz; Emmanuel Trélat; William Barbour; Dan Work; Samitha Samaranayake; Benedetto Piccoli; Qi Luo; Ryan Weightman; Sean T. McQuade; Mateo Díaz; Emmanuel Trélat; William Barbour; Dan Work; Samitha Samaranayake; Benedetto Piccoli

doi:10.3934/nhm.2022016

Networks and Heterogeneous Media

2022, Volume 17, Issue 3: 443-466. doi: 10.3934/nhm.2022016

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

1.
Department of Industrial Engineering, Clemson University, Clemson, SC, USA
2.
Center for Computational and Integrative Biology, Rutgers Camden, Camden NJ, USA
3.
Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125, USA
4.
Sorbonne Université, CNRS, Université Paris Cité, Inria, Laboratoire Jacques-Louis Lions (LJLL), F-75005 Paris, France
5.
Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, TN, USA
6.
School of Civil and Environmental Engineering, Cornell University, Ithaca, NY, USA
7.
Joseph and Loretta Lopez chair professor of Mathematics, Center for Computational and Integrative Biology, Rutgers Camden, Camden NJ, USA

Received: 01 September 2021 Revised: 01 January 2022 Published: 31 March 2022
Primary: 58F15, 58F17; Secondary: 53C35

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.
- COVID-19,
- SARS-CoV-2,
- SEIR compartmental models,
- vaccine,
- optimal control
Citation: Qi Luo, Ryan Weightman, Sean T. McQuade, Mateo Díaz, Emmanuel Trélat, William Barbour, Dan Work, Samitha Samaranayake, Benedetto Piccoli. Optimization of vaccination for COVID-19 in the midst of a pandemic[J]. Networks and Heterogeneous Media, 2022, 17(3): 443-466. doi: 10.3934/nhm.2022016

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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.

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