Policy resistance undermines superspreader vaccination strategies for influenza.

Theoretical models of infection spread on networks predict that targeting vaccination at individuals with a very large number of contacts (superspreaders) can reduce infection incidence by a significant margin. These models generally assume that superspreaders will always agree to be vaccinated. Hen...

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Autores principales:	Chad R Wells, Eili Y Klein, Chris T Bauch
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/51b85c6436e540b5abe4eab9cdb7647e
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spelling	oai:doaj.org-article:51b85c6436e540b5abe4eab9cdb7647e2021-11-18T05:52:23ZPolicy resistance undermines superspreader vaccination strategies for influenza.1553-734X1553-735810.1371/journal.pcbi.1002945https://doaj.org/article/51b85c6436e540b5abe4eab9cdb7647e2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23505357/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Theoretical models of infection spread on networks predict that targeting vaccination at individuals with a very large number of contacts (superspreaders) can reduce infection incidence by a significant margin. These models generally assume that superspreaders will always agree to be vaccinated. Hence, they cannot capture unintended consequences such as policy resistance, where the behavioral response induced by a new vaccine policy tends to reduce the expected benefits of the policy. Here, we couple a model of influenza transmission on an empirically-based contact network with a psychologically structured model of influenza vaccinating behavior, where individual vaccinating decisions depend on social learning and past experiences of perceived infections, vaccine complications and vaccine failures. We find that policy resistance almost completely undermines the effectiveness of superspreader strategies: the most commonly explored approaches that target a randomly chosen neighbor of an individual, or that preferentially choose neighbors with many contacts, provide at best a 2% relative improvement over their non-targeted counterpart as compared to 12% when behavioral feedbacks are ignored. Increased vaccine coverage in super spreaders is offset by decreased coverage in non-superspreaders, and superspreaders also have a higher rate of perceived vaccine failures on account of being infected more often. Including incentives for vaccination provides modest improvements in outcomes. We conclude that the design of influenza vaccine strategies involving widespread incentive use and/or targeting of superspreaders should account for policy resistance, and mitigate it whenever possible.Chad R WellsEili Y KleinChris T BauchPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 3, p e1002945 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Chad R Wells Eili Y Klein Chris T Bauch Policy resistance undermines superspreader vaccination strategies for influenza.
description	Theoretical models of infection spread on networks predict that targeting vaccination at individuals with a very large number of contacts (superspreaders) can reduce infection incidence by a significant margin. These models generally assume that superspreaders will always agree to be vaccinated. Hence, they cannot capture unintended consequences such as policy resistance, where the behavioral response induced by a new vaccine policy tends to reduce the expected benefits of the policy. Here, we couple a model of influenza transmission on an empirically-based contact network with a psychologically structured model of influenza vaccinating behavior, where individual vaccinating decisions depend on social learning and past experiences of perceived infections, vaccine complications and vaccine failures. We find that policy resistance almost completely undermines the effectiveness of superspreader strategies: the most commonly explored approaches that target a randomly chosen neighbor of an individual, or that preferentially choose neighbors with many contacts, provide at best a 2% relative improvement over their non-targeted counterpart as compared to 12% when behavioral feedbacks are ignored. Increased vaccine coverage in super spreaders is offset by decreased coverage in non-superspreaders, and superspreaders also have a higher rate of perceived vaccine failures on account of being infected more often. Including incentives for vaccination provides modest improvements in outcomes. We conclude that the design of influenza vaccine strategies involving widespread incentive use and/or targeting of superspreaders should account for policy resistance, and mitigate it whenever possible.
format	article
author	Chad R Wells Eili Y Klein Chris T Bauch
author_facet	Chad R Wells Eili Y Klein Chris T Bauch
author_sort	Chad R Wells
title	Policy resistance undermines superspreader vaccination strategies for influenza.
title_short	Policy resistance undermines superspreader vaccination strategies for influenza.
title_full	Policy resistance undermines superspreader vaccination strategies for influenza.
title_fullStr	Policy resistance undermines superspreader vaccination strategies for influenza.
title_full_unstemmed	Policy resistance undermines superspreader vaccination strategies for influenza.
title_sort	policy resistance undermines superspreader vaccination strategies for influenza.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/51b85c6436e540b5abe4eab9cdb7647e
work_keys_str_mv	AT chadrwells policyresistanceunderminessuperspreadervaccinationstrategiesforinfluenza AT eiliyklein policyresistanceunderminessuperspreadervaccinationstrategiesforinfluenza AT christbauch policyresistanceunderminessuperspreadervaccinationstrategiesforinfluenza
_version_	1718424724911947776

Policy resistance undermines superspreader vaccination strategies for influenza.

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