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Optimal strategies to protect a sub-population at risk due to an established epidemic

Epidemics can particularly threaten certain sub-populations. For example, for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the elderly are often preferentially protected. For diseases of plants and animals, certain sub-populations can drive mitigation because they are intrinsically...

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Autores principales: Bussell, Elliott H., Cunniffe, Nik J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8753150/
https://www.ncbi.nlm.nih.gov/pubmed/35016554
http://dx.doi.org/10.1098/rsif.2021.0718
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author Bussell, Elliott H.
Cunniffe, Nik J.
author_facet Bussell, Elliott H.
Cunniffe, Nik J.
author_sort Bussell, Elliott H.
collection PubMed
description Epidemics can particularly threaten certain sub-populations. For example, for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the elderly are often preferentially protected. For diseases of plants and animals, certain sub-populations can drive mitigation because they are intrinsically more valuable for ecological, economic, socio-cultural or political reasons. Here, we use optimal control theory to identify strategies to optimally protect a ‘high-value’ sub-population when there is a limited budget and epidemiological uncertainty. We use protection of the Redwood National Park in California in the face of the large ongoing state-wide epidemic of sudden oak death (caused by Phytophthora ramorum) as a case study. We concentrate on whether control should be focused entirely within the National Park itself, or whether treatment of the growing epidemic in the surrounding ‘buffer region’ can instead be more profitable. We find that, depending on rates of infection and the size of the ongoing epidemic, focusing control on the high-value region is often optimal. However, priority should sometimes switch from the buffer region to the high-value region only as the local outbreak grows. We characterize how the timing of any switch depends on epidemiological and logistic parameters, and test robustness to systematic misspecification of these factors due to imperfect prior knowledge.
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spelling pubmed-87531502022-01-12 Optimal strategies to protect a sub-population at risk due to an established epidemic Bussell, Elliott H. Cunniffe, Nik J. J R Soc Interface Life Sciences–Mathematics interface Epidemics can particularly threaten certain sub-populations. For example, for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the elderly are often preferentially protected. For diseases of plants and animals, certain sub-populations can drive mitigation because they are intrinsically more valuable for ecological, economic, socio-cultural or political reasons. Here, we use optimal control theory to identify strategies to optimally protect a ‘high-value’ sub-population when there is a limited budget and epidemiological uncertainty. We use protection of the Redwood National Park in California in the face of the large ongoing state-wide epidemic of sudden oak death (caused by Phytophthora ramorum) as a case study. We concentrate on whether control should be focused entirely within the National Park itself, or whether treatment of the growing epidemic in the surrounding ‘buffer region’ can instead be more profitable. We find that, depending on rates of infection and the size of the ongoing epidemic, focusing control on the high-value region is often optimal. However, priority should sometimes switch from the buffer region to the high-value region only as the local outbreak grows. We characterize how the timing of any switch depends on epidemiological and logistic parameters, and test robustness to systematic misspecification of these factors due to imperfect prior knowledge. The Royal Society 2022-01-12 /pmc/articles/PMC8753150/ /pubmed/35016554 http://dx.doi.org/10.1098/rsif.2021.0718 Text en © 2022 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited.
spellingShingle Life Sciences–Mathematics interface
Bussell, Elliott H.
Cunniffe, Nik J.
Optimal strategies to protect a sub-population at risk due to an established epidemic
title Optimal strategies to protect a sub-population at risk due to an established epidemic
title_full Optimal strategies to protect a sub-population at risk due to an established epidemic
title_fullStr Optimal strategies to protect a sub-population at risk due to an established epidemic
title_full_unstemmed Optimal strategies to protect a sub-population at risk due to an established epidemic
title_short Optimal strategies to protect a sub-population at risk due to an established epidemic
title_sort optimal strategies to protect a sub-population at risk due to an established epidemic
topic Life Sciences–Mathematics interface
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8753150/
https://www.ncbi.nlm.nih.gov/pubmed/35016554
http://dx.doi.org/10.1098/rsif.2021.0718
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