Ibrahim, D., Kis, Z., Tak, K. et al. (4 more authors) (2021) Model-based planning and delivery of mass vaccination campaigns against infectious disease: application to the COVID-19 pandemic in the UK. Vaccines, 9 (12). 1460. ISSN 2076-393X
Abstract
Vaccination plays a key role in reducing morbidity and mortality caused by infectious diseases, including the recent COVID-19 pandemic. However, a comprehensive approach that allows the planning of vaccination campaigns and the estimation of the resources required to deliver and administer COVID-19 vaccines is lacking. This work implements a new framework that supports the planning and delivery of vaccination campaigns. Firstly, the framework segments and priorities target populations, then estimates vaccination timeframe and workforce requirements, and lastly predicts logistics costs and facilitates the distribution of vaccines from manufacturing plants to vaccination centres. The outcomes from this study reveal the necessary resources required and their associated costs ahead of a vaccination campaign. Analysis of results shows that by integrating demand stratification, administration, and the supply chain, the synergy amongst these activities can be exploited to allow planning and cost-effective delivery of a vaccination campaign against COVID-19 and demonstrates how to sustain high rates of vaccination in a resource-efficient fashion.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 by the authors. 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/). |
Keywords: | SARS-CoV-2 vaccines; vaccination campaign; vaccine availability; demand stratification; economic analysis; mathematical programming |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Chemical and Biological Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 20 Dec 2021 15:27 |
Last Modified: | 20 Dec 2021 15:27 |
Status: | Published |
Publisher: | MDPI AG |
Refereed: | Yes |
Identification Number: | 10.3390/vaccines9121460 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:181790 |