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Modeling immune response and its effect on infectious disease outbreak dynamics
BACKGROUND: In recent epidemiological models, immunity is incorporated as a simplified value that determines the capacity of an individual to become infected or to transmit the disease. Moreover, the quality of the immune response determines the chances of infection and the length of time an individ...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4779228/ https://www.ncbi.nlm.nih.gov/pubmed/26944943 http://dx.doi.org/10.1186/s12976-016-0033-6 |
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author | Reyes-Silveyra, Jorge Mikler, Armin R. |
author_facet | Reyes-Silveyra, Jorge Mikler, Armin R. |
author_sort | Reyes-Silveyra, Jorge |
collection | PubMed |
description | BACKGROUND: In recent epidemiological models, immunity is incorporated as a simplified value that determines the capacity of an individual to become infected or to transmit the disease. Moreover, the quality of the immune response determines the chances of infection and the length of time an individual is capable to infect others. We present a model that incorporates individuals’ immune responses to, further, examine the role of the collective immune response of individuals in a population during an infectious outbreak. METHODS: We constructed a contagion model that incorporates the collective immune response of individuals represented by the superposition of individual immune responses (PIR). Multiple probability distributions are used to represent the immunocompetence of different age groups, thereby modeling the concept of Population Immune Response (PIR). Multiple experiments were conducted in which the population is divided in different age groups for which each group has a unique immune response quality and thus a different length for its immune periods. Finally, we explored the effects of implementing different vaccination strategies in the population. RESULTS: The experiments displayed important variations in the outbreak dynamics as a consequence of incorporating PIR in homogeneous and mixed populations. The experiments showed that individuals with weak immune responses and those who are immune to the pathogen play a significant role in shaping the outbreak dynamics. Finally, after implementing different vaccination strategies, the results suggest that if vaccination resources are limited, the vaccination should be targeted towards individuals that spread the disease for a longer period of time. CONCLUSIONS: Our results suggest that it is essential for the public health establishment to increase their understanding of the characteristics of regional demographics that could impact the quality of the immune response of the individuals. The results indicate that it is necessary to further investigate mitigation strategies to limit the capacity to transmit the disease by individuals that spread the pathogen for extended periods of time. Ultimately, this study suggests that it is crucial for public health researchers to identify appropriate targeted vaccination regimes and to explore the link between PIR and outbreak dynamics to improve the monitoring and mitigating efforts of ongoing and future epidemics. |
format | Online Article Text |
id | pubmed-4779228 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-47792282016-03-06 Modeling immune response and its effect on infectious disease outbreak dynamics Reyes-Silveyra, Jorge Mikler, Armin R. Theor Biol Med Model Research BACKGROUND: In recent epidemiological models, immunity is incorporated as a simplified value that determines the capacity of an individual to become infected or to transmit the disease. Moreover, the quality of the immune response determines the chances of infection and the length of time an individual is capable to infect others. We present a model that incorporates individuals’ immune responses to, further, examine the role of the collective immune response of individuals in a population during an infectious outbreak. METHODS: We constructed a contagion model that incorporates the collective immune response of individuals represented by the superposition of individual immune responses (PIR). Multiple probability distributions are used to represent the immunocompetence of different age groups, thereby modeling the concept of Population Immune Response (PIR). Multiple experiments were conducted in which the population is divided in different age groups for which each group has a unique immune response quality and thus a different length for its immune periods. Finally, we explored the effects of implementing different vaccination strategies in the population. RESULTS: The experiments displayed important variations in the outbreak dynamics as a consequence of incorporating PIR in homogeneous and mixed populations. The experiments showed that individuals with weak immune responses and those who are immune to the pathogen play a significant role in shaping the outbreak dynamics. Finally, after implementing different vaccination strategies, the results suggest that if vaccination resources are limited, the vaccination should be targeted towards individuals that spread the disease for a longer period of time. CONCLUSIONS: Our results suggest that it is essential for the public health establishment to increase their understanding of the characteristics of regional demographics that could impact the quality of the immune response of the individuals. The results indicate that it is necessary to further investigate mitigation strategies to limit the capacity to transmit the disease by individuals that spread the pathogen for extended periods of time. Ultimately, this study suggests that it is crucial for public health researchers to identify appropriate targeted vaccination regimes and to explore the link between PIR and outbreak dynamics to improve the monitoring and mitigating efforts of ongoing and future epidemics. BioMed Central 2016-03-05 /pmc/articles/PMC4779228/ /pubmed/26944943 http://dx.doi.org/10.1186/s12976-016-0033-6 Text en © Reyes-Silveyra and Mikler. 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Reyes-Silveyra, Jorge Mikler, Armin R. Modeling immune response and its effect on infectious disease outbreak dynamics |
title | Modeling immune response and its effect on infectious disease outbreak dynamics |
title_full | Modeling immune response and its effect on infectious disease outbreak dynamics |
title_fullStr | Modeling immune response and its effect on infectious disease outbreak dynamics |
title_full_unstemmed | Modeling immune response and its effect on infectious disease outbreak dynamics |
title_short | Modeling immune response and its effect on infectious disease outbreak dynamics |
title_sort | modeling immune response and its effect on infectious disease outbreak dynamics |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4779228/ https://www.ncbi.nlm.nih.gov/pubmed/26944943 http://dx.doi.org/10.1186/s12976-016-0033-6 |
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