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Modeling influenza transmission dynamics with media coverage data of the 2009 H1N1 outbreak in Korea
Recurrent outbreaks of the influenza virus continue to pose a serious health threat all over the world. The role of mass media becomes increasingly important in modeling infectious disease transmission dynamics since it can provide public health information that influences risk perception and health...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7289370/ https://www.ncbi.nlm.nih.gov/pubmed/32525907 http://dx.doi.org/10.1371/journal.pone.0232580 |
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author | Kim, Yunhwan Barber, Ana Vivas Lee, Sunmi |
author_facet | Kim, Yunhwan Barber, Ana Vivas Lee, Sunmi |
author_sort | Kim, Yunhwan |
collection | PubMed |
description | Recurrent outbreaks of the influenza virus continue to pose a serious health threat all over the world. The role of mass media becomes increasingly important in modeling infectious disease transmission dynamics since it can provide public health information that influences risk perception and health behaviors. Motivated by the recent 2009 H1N1 influenza pandemic outbreak in South Korea, a mathematical model has been developed. In this work, a previous influenza transmission model is modified by incorporating two distinct media effect terms in the transmission rate function; (1) a theory-based media effect term is defined as a function of the number of infected people and its rage of change and (2) a data-based media effect term employs the real-world media coverage data during the same period of the 2009 influenza outbreak. The transmission rate and the media parameters are estimated through the least-squares fitting of the influenza model with two media effect terms to the 2009 H1N1 cumulative number of confirmed cases. The impacts of media effect terms are investigated in terms of incidence and cumulative incidence. Our results highlight that the theory-based and data-based media effect terms have almost the same influence on the influenza dynamics under the parameters obtained in this study. Numerical simulations suggest that the media can have a positive influence on influenza dynamics; more media coverage leads to a reduced peak size and final epidemic size of influenza. |
format | Online Article Text |
id | pubmed-7289370 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-72893702020-06-15 Modeling influenza transmission dynamics with media coverage data of the 2009 H1N1 outbreak in Korea Kim, Yunhwan Barber, Ana Vivas Lee, Sunmi PLoS One Research Article Recurrent outbreaks of the influenza virus continue to pose a serious health threat all over the world. The role of mass media becomes increasingly important in modeling infectious disease transmission dynamics since it can provide public health information that influences risk perception and health behaviors. Motivated by the recent 2009 H1N1 influenza pandemic outbreak in South Korea, a mathematical model has been developed. In this work, a previous influenza transmission model is modified by incorporating two distinct media effect terms in the transmission rate function; (1) a theory-based media effect term is defined as a function of the number of infected people and its rage of change and (2) a data-based media effect term employs the real-world media coverage data during the same period of the 2009 influenza outbreak. The transmission rate and the media parameters are estimated through the least-squares fitting of the influenza model with two media effect terms to the 2009 H1N1 cumulative number of confirmed cases. The impacts of media effect terms are investigated in terms of incidence and cumulative incidence. Our results highlight that the theory-based and data-based media effect terms have almost the same influence on the influenza dynamics under the parameters obtained in this study. Numerical simulations suggest that the media can have a positive influence on influenza dynamics; more media coverage leads to a reduced peak size and final epidemic size of influenza. Public Library of Science 2020-06-11 /pmc/articles/PMC7289370/ /pubmed/32525907 http://dx.doi.org/10.1371/journal.pone.0232580 Text en © 2020 Kim 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 (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 Kim, Yunhwan Barber, Ana Vivas Lee, Sunmi Modeling influenza transmission dynamics with media coverage data of the 2009 H1N1 outbreak in Korea |
title | Modeling influenza transmission dynamics with media coverage data of the 2009 H1N1 outbreak in Korea |
title_full | Modeling influenza transmission dynamics with media coverage data of the 2009 H1N1 outbreak in Korea |
title_fullStr | Modeling influenza transmission dynamics with media coverage data of the 2009 H1N1 outbreak in Korea |
title_full_unstemmed | Modeling influenza transmission dynamics with media coverage data of the 2009 H1N1 outbreak in Korea |
title_short | Modeling influenza transmission dynamics with media coverage data of the 2009 H1N1 outbreak in Korea |
title_sort | modeling influenza transmission dynamics with media coverage data of the 2009 h1n1 outbreak in korea |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7289370/ https://www.ncbi.nlm.nih.gov/pubmed/32525907 http://dx.doi.org/10.1371/journal.pone.0232580 |
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