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Allocating epidemic response teams and vaccine deliveries by drone in generic network structures, according to expected prevented exposures

The tumultuous inception of an epidemic is usually accompanied by difficulty in determining how to respond best. In developing nations, this can be compounded by logistical challenges, such as vaccine shortages and poor road infrastructure. To provide guidance towards improved epidemic response, var...

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Autores principales: Matter, Dean, Potgieter, Linke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7935281/
https://www.ncbi.nlm.nih.gov/pubmed/33667263
http://dx.doi.org/10.1371/journal.pone.0248053
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author Matter, Dean
Potgieter, Linke
author_facet Matter, Dean
Potgieter, Linke
author_sort Matter, Dean
collection PubMed
description The tumultuous inception of an epidemic is usually accompanied by difficulty in determining how to respond best. In developing nations, this can be compounded by logistical challenges, such as vaccine shortages and poor road infrastructure. To provide guidance towards improved epidemic response, various resource allocation models, in conjunction with a network-based SEIRVD epidemic model, are proposed in this article. Further, the feasibility of using drones for vaccine delivery is evaluated, and assorted relevant parameters are discussed. For the sake of generality, these results are presented for multiple network structures, representing interconnected populations—upon which repeated epidemic simulations are performed. The resource allocation models formulated maximise expected prevented exposures on each day of a simulated epidemic, by allocating response teams and vaccine deliveries according to the solutions of two respective integer programming problems—thereby influencing the simulated epidemic through the SEIRVD model. These models, when compared with a range of alternative resource allocation strategies, were found to reduce both the number of cases per epidemic, and the number of vaccines required. Consequently, the recommendation is made that such models be used as decision support tools in epidemic response. In the absence thereof, prioritizing locations for vaccinations according to susceptible population, rather than total population or number of infections, is most effective for the majority of network types. In other results, fixed-wing drones are demonstrated to be a viable delivery method for vaccines in the context of an epidemic, if sufficient drones can be promptly procured; the detrimental effect of intervention delay was discovered to be significant. In addition, the importance of well-documented routine vaccination activities is highlighted, due to the benefits of increased pre-epidemic immunity rates, and targeted vaccination.
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spelling pubmed-79352812021-03-15 Allocating epidemic response teams and vaccine deliveries by drone in generic network structures, according to expected prevented exposures Matter, Dean Potgieter, Linke PLoS One Research Article The tumultuous inception of an epidemic is usually accompanied by difficulty in determining how to respond best. In developing nations, this can be compounded by logistical challenges, such as vaccine shortages and poor road infrastructure. To provide guidance towards improved epidemic response, various resource allocation models, in conjunction with a network-based SEIRVD epidemic model, are proposed in this article. Further, the feasibility of using drones for vaccine delivery is evaluated, and assorted relevant parameters are discussed. For the sake of generality, these results are presented for multiple network structures, representing interconnected populations—upon which repeated epidemic simulations are performed. The resource allocation models formulated maximise expected prevented exposures on each day of a simulated epidemic, by allocating response teams and vaccine deliveries according to the solutions of two respective integer programming problems—thereby influencing the simulated epidemic through the SEIRVD model. These models, when compared with a range of alternative resource allocation strategies, were found to reduce both the number of cases per epidemic, and the number of vaccines required. Consequently, the recommendation is made that such models be used as decision support tools in epidemic response. In the absence thereof, prioritizing locations for vaccinations according to susceptible population, rather than total population or number of infections, is most effective for the majority of network types. In other results, fixed-wing drones are demonstrated to be a viable delivery method for vaccines in the context of an epidemic, if sufficient drones can be promptly procured; the detrimental effect of intervention delay was discovered to be significant. In addition, the importance of well-documented routine vaccination activities is highlighted, due to the benefits of increased pre-epidemic immunity rates, and targeted vaccination. Public Library of Science 2021-03-05 /pmc/articles/PMC7935281/ /pubmed/33667263 http://dx.doi.org/10.1371/journal.pone.0248053 Text en © 2021 Matter, Potgieter http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Matter, Dean
Potgieter, Linke
Allocating epidemic response teams and vaccine deliveries by drone in generic network structures, according to expected prevented exposures
title Allocating epidemic response teams and vaccine deliveries by drone in generic network structures, according to expected prevented exposures
title_full Allocating epidemic response teams and vaccine deliveries by drone in generic network structures, according to expected prevented exposures
title_fullStr Allocating epidemic response teams and vaccine deliveries by drone in generic network structures, according to expected prevented exposures
title_full_unstemmed Allocating epidemic response teams and vaccine deliveries by drone in generic network structures, according to expected prevented exposures
title_short Allocating epidemic response teams and vaccine deliveries by drone in generic network structures, according to expected prevented exposures
title_sort allocating epidemic response teams and vaccine deliveries by drone in generic network structures, according to expected prevented exposures
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7935281/
https://www.ncbi.nlm.nih.gov/pubmed/33667263
http://dx.doi.org/10.1371/journal.pone.0248053
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