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Spatial Optimization to Improve COVID-19 Vaccine Allocation
Early distribution of COVID-19 vaccines was largely driven by population size and did not account for COVID-19 prevalence nor location characteristics. In this study, we applied an optimization framework to identify distribution strategies that would have lowered COVID-19 related morbidity and morta...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9866695/ https://www.ncbi.nlm.nih.gov/pubmed/36679909 http://dx.doi.org/10.3390/vaccines11010064 |
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author | Scroggins, Stephen Goodson, Justin Afroze, Tasnova Shacham, Enbal |
author_facet | Scroggins, Stephen Goodson, Justin Afroze, Tasnova Shacham, Enbal |
author_sort | Scroggins, Stephen |
collection | PubMed |
description | Early distribution of COVID-19 vaccines was largely driven by population size and did not account for COVID-19 prevalence nor location characteristics. In this study, we applied an optimization framework to identify distribution strategies that would have lowered COVID-19 related morbidity and mortality. During the first half of 2021 in the state of Missouri, optimized vaccine allocation would have decreased case incidence by 8% with 5926 fewer COVID-19 cases, 106 fewer deaths, and 4.5 million dollars in healthcare cost saved. As COVID-19 variants continue to be identified, and the likelihood of future pandemics remains high, application of resource optimization should be a priority for policy makers. |
format | Online Article Text |
id | pubmed-9866695 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98666952023-01-22 Spatial Optimization to Improve COVID-19 Vaccine Allocation Scroggins, Stephen Goodson, Justin Afroze, Tasnova Shacham, Enbal Vaccines (Basel) Article Early distribution of COVID-19 vaccines was largely driven by population size and did not account for COVID-19 prevalence nor location characteristics. In this study, we applied an optimization framework to identify distribution strategies that would have lowered COVID-19 related morbidity and mortality. During the first half of 2021 in the state of Missouri, optimized vaccine allocation would have decreased case incidence by 8% with 5926 fewer COVID-19 cases, 106 fewer deaths, and 4.5 million dollars in healthcare cost saved. As COVID-19 variants continue to be identified, and the likelihood of future pandemics remains high, application of resource optimization should be a priority for policy makers. MDPI 2022-12-28 /pmc/articles/PMC9866695/ /pubmed/36679909 http://dx.doi.org/10.3390/vaccines11010064 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Scroggins, Stephen Goodson, Justin Afroze, Tasnova Shacham, Enbal Spatial Optimization to Improve COVID-19 Vaccine Allocation |
title | Spatial Optimization to Improve COVID-19 Vaccine Allocation |
title_full | Spatial Optimization to Improve COVID-19 Vaccine Allocation |
title_fullStr | Spatial Optimization to Improve COVID-19 Vaccine Allocation |
title_full_unstemmed | Spatial Optimization to Improve COVID-19 Vaccine Allocation |
title_short | Spatial Optimization to Improve COVID-19 Vaccine Allocation |
title_sort | spatial optimization to improve covid-19 vaccine allocation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9866695/ https://www.ncbi.nlm.nih.gov/pubmed/36679909 http://dx.doi.org/10.3390/vaccines11010064 |
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