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

doi:10.3934/nhm.2022016

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

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

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.

Original language	English (US)
Pages (from-to)	443-466
Number of pages	24
Journal	Networks and Heterogeneous Media
Volume	17
Issue number	3
DOIs	https://doi.org/10.3934/nhm.2022016
State	Published - Jun 2022
Externally published	Yes

All Science Journal Classification (ASJC) codes

Statistics and Probability
Engineering(all)
Computer Science Applications
Applied Mathematics

Keywords

COVID-19
SARS-CoV-2
SEIR compartmental models
optimal control
vaccine

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10.3934/nhm.2022016

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title = "OPTIMIZATION OF VACCINATION FOR COVID-19 IN THE MIDST OF A PANDEMIC",

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.",

keywords = "COVID-19, SARS-CoV-2, SEIR compartmental models, optimal control, vaccine",

author = "Qi Luo and Ryan Weightman and McQuade, {Sean T.} and Mateo D{\'i}az and Emmanuel Tr{\'e}lat and William Barbour and Dan Work and Samitha Samaranayake and Benedetto Piccoli",

note = "Funding Information: 2020 Mathematics Subject Classification. Primary: 58F15, 58F17; Secondary: 53C35. Key words and phrases. COVID-19; SARS-CoV-2; SEIR compartmental models; vaccine; optimal control. The authors acknowledge the support of the NSF CMMI project # 2033580 “Managing pandemic by managing mobility”. R.W., S.T.M. and B.P. acknowledge the support of the Joseph and Loretta Lopez Chair endowment. Publisher Copyright: {\textcopyright} 2022, American Institute of Mathematical Sciences. All rights reserved.",

year = "2022",

month = jun,

doi = "10.3934/nhm.2022016",

language = "English (US)",

volume = "17",

pages = "443--466",

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publisher = "American Institute of Mathematical Sciences",

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AU - Luo, Qi

AU - Weightman, Ryan

AU - McQuade, Sean T.

AU - Díaz, Mateo

AU - Trélat, Emmanuel

AU - Barbour, William

AU - Work, Dan

AU - Samaranayake, Samitha

AU - Piccoli, Benedetto

N1 - Funding Information: 2020 Mathematics Subject Classification. Primary: 58F15, 58F17; Secondary: 53C35. Key words and phrases. COVID-19; SARS-CoV-2; SEIR compartmental models; vaccine; optimal control. The authors acknowledge the support of the NSF CMMI project # 2033580 “Managing pandemic by managing mobility”. R.W., S.T.M. and B.P. acknowledge the support of the Joseph and Loretta Lopez Chair endowment. Publisher Copyright: © 2022, American Institute of Mathematical Sciences. All rights reserved.

PY - 2022/6

Y1 - 2022/6

N2 - 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.

AB - 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.

KW - COVID-19

KW - SARS-CoV-2

KW - SEIR compartmental models

KW - optimal control

KW - vaccine

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OPTIMIZATION OF VACCINATION FOR COVID-19 IN THE MIDST OF A PANDEMIC

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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