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Effect of density dependence on coinfection dynamics
In this paper we develop a compartmental model of SIR type (the abbreviation refers to the number of Susceptible, Infected and Recovered people) that models the population dynamics of two diseases that can coinfect. We discuss how the underlying dynamics depends on the carrying capacity K: from a si...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8452503/ https://www.ncbi.nlm.nih.gov/pubmed/34566882 http://dx.doi.org/10.1007/s13324-021-00570-9 |
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author | Andersson, Jonathan Ghersheen, Samia Kozlov, Vladimir Tkachev, Vladimir G. Wennergren, Uno |
author_facet | Andersson, Jonathan Ghersheen, Samia Kozlov, Vladimir Tkachev, Vladimir G. Wennergren, Uno |
author_sort | Andersson, Jonathan |
collection | PubMed |
description | In this paper we develop a compartmental model of SIR type (the abbreviation refers to the number of Susceptible, Infected and Recovered people) that models the population dynamics of two diseases that can coinfect. We discuss how the underlying dynamics depends on the carrying capacity K: from a simple dynamics to a more complex. This can also help in understanding the appearance of more complicated dynamics, for example, chaos and periodic oscillations, for large values of K. It is also presented that pathogens can invade in population and their invasion depends on the carrying capacity K which shows that the progression of disease in population depends on carrying capacity. More specifically, we establish all possible scenarios (the so-called transition diagrams) describing an evolution of an (always unique) locally stable equilibrium state (with only non-negative compartments) for fixed fundamental parameters (density independent transmission and vital rates) as a function of the carrying capacity K. An important implication of our results is the following important observation. Note that one can regard the value of K as the natural ‘size’ (the capacity) of a habitat. From this point of view, an isolation of individuals (the strategy which showed its efficiency for COVID-19 in various countries) into smaller resp. larger groups can be modelled by smaller resp. bigger values of K. Then we conclude that the infection dynamics becomes more complex for larger groups, as it fairly maybe expected for values of the reproduction number [Formula: see text] . We show even more, that for the values [Formula: see text] there are several (in fact four different) distinguished scenarios where the infection complexity (the number of nonzero infected classes) arises with growing K. Our approach is based on a bifurcation analysis which allows to generalize considerably the previous Lotka-Volterra model considered previously in Ghersheen et al. (Math Meth Appl Sci 42(8), 2019). |
format | Online Article Text |
id | pubmed-8452503 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-84525032021-09-21 Effect of density dependence on coinfection dynamics Andersson, Jonathan Ghersheen, Samia Kozlov, Vladimir Tkachev, Vladimir G. Wennergren, Uno Anal Math Phys Article In this paper we develop a compartmental model of SIR type (the abbreviation refers to the number of Susceptible, Infected and Recovered people) that models the population dynamics of two diseases that can coinfect. We discuss how the underlying dynamics depends on the carrying capacity K: from a simple dynamics to a more complex. This can also help in understanding the appearance of more complicated dynamics, for example, chaos and periodic oscillations, for large values of K. It is also presented that pathogens can invade in population and their invasion depends on the carrying capacity K which shows that the progression of disease in population depends on carrying capacity. More specifically, we establish all possible scenarios (the so-called transition diagrams) describing an evolution of an (always unique) locally stable equilibrium state (with only non-negative compartments) for fixed fundamental parameters (density independent transmission and vital rates) as a function of the carrying capacity K. An important implication of our results is the following important observation. Note that one can regard the value of K as the natural ‘size’ (the capacity) of a habitat. From this point of view, an isolation of individuals (the strategy which showed its efficiency for COVID-19 in various countries) into smaller resp. larger groups can be modelled by smaller resp. bigger values of K. Then we conclude that the infection dynamics becomes more complex for larger groups, as it fairly maybe expected for values of the reproduction number [Formula: see text] . We show even more, that for the values [Formula: see text] there are several (in fact four different) distinguished scenarios where the infection complexity (the number of nonzero infected classes) arises with growing K. Our approach is based on a bifurcation analysis which allows to generalize considerably the previous Lotka-Volterra model considered previously in Ghersheen et al. (Math Meth Appl Sci 42(8), 2019). Springer International Publishing 2021-09-21 2021 /pmc/articles/PMC8452503/ /pubmed/34566882 http://dx.doi.org/10.1007/s13324-021-00570-9 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Andersson, Jonathan Ghersheen, Samia Kozlov, Vladimir Tkachev, Vladimir G. Wennergren, Uno Effect of density dependence on coinfection dynamics |
title | Effect of density dependence on coinfection dynamics |
title_full | Effect of density dependence on coinfection dynamics |
title_fullStr | Effect of density dependence on coinfection dynamics |
title_full_unstemmed | Effect of density dependence on coinfection dynamics |
title_short | Effect of density dependence on coinfection dynamics |
title_sort | effect of density dependence on coinfection dynamics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8452503/ https://www.ncbi.nlm.nih.gov/pubmed/34566882 http://dx.doi.org/10.1007/s13324-021-00570-9 |
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