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Dynamics of an epidemic model with spatial diffusion
Mathematical models are very useful in analyzing the spread and control of infectious diseases which can be used to predict the developing tendency of the infectious disease, determine the key factors and to seek the optimum strategies of disease control. As a result, we investigated the pattern dyn...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier B.V.
2014
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7185886/ https://www.ncbi.nlm.nih.gov/pubmed/32362717 http://dx.doi.org/10.1016/j.physa.2014.04.028 |
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author | Wang, Tao |
author_facet | Wang, Tao |
author_sort | Wang, Tao |
collection | PubMed |
description | Mathematical models are very useful in analyzing the spread and control of infectious diseases which can be used to predict the developing tendency of the infectious disease, determine the key factors and to seek the optimum strategies of disease control. As a result, we investigated the pattern dynamics of a spatial epidemic model with logistic growth. By using amplitude equation, we found that there were different types of stationary patterns including spotted, mixed, and stripe patterns, which mean that spatial motion of individuals can form high density of diseases. The obtained results can be extended in other related fields, such as vegetation patterns in ecosystems. |
format | Online Article Text |
id | pubmed-7185886 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71858862020-04-28 Dynamics of an epidemic model with spatial diffusion Wang, Tao Physica A Article Mathematical models are very useful in analyzing the spread and control of infectious diseases which can be used to predict the developing tendency of the infectious disease, determine the key factors and to seek the optimum strategies of disease control. As a result, we investigated the pattern dynamics of a spatial epidemic model with logistic growth. By using amplitude equation, we found that there were different types of stationary patterns including spotted, mixed, and stripe patterns, which mean that spatial motion of individuals can form high density of diseases. The obtained results can be extended in other related fields, such as vegetation patterns in ecosystems. Elsevier B.V. 2014-09-01 2014-04-26 /pmc/articles/PMC7185886/ /pubmed/32362717 http://dx.doi.org/10.1016/j.physa.2014.04.028 Text en Copyright © 2014 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Wang, Tao Dynamics of an epidemic model with spatial diffusion |
title | Dynamics of an epidemic model with spatial diffusion |
title_full | Dynamics of an epidemic model with spatial diffusion |
title_fullStr | Dynamics of an epidemic model with spatial diffusion |
title_full_unstemmed | Dynamics of an epidemic model with spatial diffusion |
title_short | Dynamics of an epidemic model with spatial diffusion |
title_sort | dynamics of an epidemic model with spatial diffusion |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7185886/ https://www.ncbi.nlm.nih.gov/pubmed/32362717 http://dx.doi.org/10.1016/j.physa.2014.04.028 |
work_keys_str_mv | AT wangtao dynamicsofanepidemicmodelwithspatialdiffusion |