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A Perspective on Multiple Waves of Influenza Pandemics

BACKGROUND: A striking characteristic of the past four influenza pandemic outbreaks in the United States has been the multiple waves of infections. However, the mechanisms responsible for the multiple waves of influenza or other acute infectious diseases are uncertain. Understanding these mechanisms...

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Autores principales: Mummert, Anna, Weiss, Howard, Long, Li-Ping, Amigó, José M., Wan, Xiu-Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3634039/
https://www.ncbi.nlm.nih.gov/pubmed/23637746
http://dx.doi.org/10.1371/journal.pone.0060343
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author Mummert, Anna
Weiss, Howard
Long, Li-Ping
Amigó, José M.
Wan, Xiu-Feng
author_facet Mummert, Anna
Weiss, Howard
Long, Li-Ping
Amigó, José M.
Wan, Xiu-Feng
author_sort Mummert, Anna
collection PubMed
description BACKGROUND: A striking characteristic of the past four influenza pandemic outbreaks in the United States has been the multiple waves of infections. However, the mechanisms responsible for the multiple waves of influenza or other acute infectious diseases are uncertain. Understanding these mechanisms could provide knowledge for health authorities to develop and implement prevention and control strategies. MATERIALS AND METHODS: We exhibit five distinct mechanisms, each of which can generate two waves of infections for an acute infectious disease. The first two mechanisms capture changes in virus transmissibility and behavioral changes. The third mechanism involves population heterogeneity (e.g., demography, geography), where each wave spreads through one sub-population. The fourth mechanism is virus mutation which causes delayed susceptibility of individuals. The fifth mechanism is waning immunity. Each mechanism is incorporated into separate mathematical models, and outbreaks are then simulated. We use the models to examine the effects of the initial number of infected individuals (e.g., border control at the beginning of the outbreak) and the timing of and amount of available vaccinations. RESULTS: Four models, individually or in any combination, reproduce the two waves of the 2009 H1N1 pandemic in the United States, both qualitatively and quantitatively. One model reproduces the two waves only qualitatively. All models indicate that significantly reducing or delaying the initial numbers of infected individuals would have little impact on the attack rate. Instead, this reduction or delay results in a single wave as opposed to two waves. Furthermore, four of these models also indicate that a vaccination program started earlier than October 2009 (when the H1N1 vaccine was initially distributed) could have eliminated the second wave of infection, while more vaccine available starting in October would not have eliminated the second wave.
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spelling pubmed-36340392013-05-01 A Perspective on Multiple Waves of Influenza Pandemics Mummert, Anna Weiss, Howard Long, Li-Ping Amigó, José M. Wan, Xiu-Feng PLoS One Research Article BACKGROUND: A striking characteristic of the past four influenza pandemic outbreaks in the United States has been the multiple waves of infections. However, the mechanisms responsible for the multiple waves of influenza or other acute infectious diseases are uncertain. Understanding these mechanisms could provide knowledge for health authorities to develop and implement prevention and control strategies. MATERIALS AND METHODS: We exhibit five distinct mechanisms, each of which can generate two waves of infections for an acute infectious disease. The first two mechanisms capture changes in virus transmissibility and behavioral changes. The third mechanism involves population heterogeneity (e.g., demography, geography), where each wave spreads through one sub-population. The fourth mechanism is virus mutation which causes delayed susceptibility of individuals. The fifth mechanism is waning immunity. Each mechanism is incorporated into separate mathematical models, and outbreaks are then simulated. We use the models to examine the effects of the initial number of infected individuals (e.g., border control at the beginning of the outbreak) and the timing of and amount of available vaccinations. RESULTS: Four models, individually or in any combination, reproduce the two waves of the 2009 H1N1 pandemic in the United States, both qualitatively and quantitatively. One model reproduces the two waves only qualitatively. All models indicate that significantly reducing or delaying the initial numbers of infected individuals would have little impact on the attack rate. Instead, this reduction or delay results in a single wave as opposed to two waves. Furthermore, four of these models also indicate that a vaccination program started earlier than October 2009 (when the H1N1 vaccine was initially distributed) could have eliminated the second wave of infection, while more vaccine available starting in October would not have eliminated the second wave. Public Library of Science 2013-04-23 /pmc/articles/PMC3634039/ /pubmed/23637746 http://dx.doi.org/10.1371/journal.pone.0060343 Text en © 2013 Mummert et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Mummert, Anna
Weiss, Howard
Long, Li-Ping
Amigó, José M.
Wan, Xiu-Feng
A Perspective on Multiple Waves of Influenza Pandemics
title A Perspective on Multiple Waves of Influenza Pandemics
title_full A Perspective on Multiple Waves of Influenza Pandemics
title_fullStr A Perspective on Multiple Waves of Influenza Pandemics
title_full_unstemmed A Perspective on Multiple Waves of Influenza Pandemics
title_short A Perspective on Multiple Waves of Influenza Pandemics
title_sort perspective on multiple waves of influenza pandemics
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3634039/
https://www.ncbi.nlm.nih.gov/pubmed/23637746
http://dx.doi.org/10.1371/journal.pone.0060343
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