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Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach

BACKGROUND: The spread of an infectious disease is determined by biological and social factors. Models based on cellular automata are adequate to describe such natural systems consisting of a massive collection of simple interacting objects. They characterize the time evolution of the global system...

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Autores principales: López, Leonardo, Burguerner, Germán, Giovanini, Leonardo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4022236/
https://www.ncbi.nlm.nih.gov/pubmed/24725804
http://dx.doi.org/10.1186/1756-0500-7-234
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author López, Leonardo
Burguerner, Germán
Giovanini, Leonardo
author_facet López, Leonardo
Burguerner, Germán
Giovanini, Leonardo
author_sort López, Leonardo
collection PubMed
description BACKGROUND: The spread of an infectious disease is determined by biological and social factors. Models based on cellular automata are adequate to describe such natural systems consisting of a massive collection of simple interacting objects. They characterize the time evolution of the global system as the emergent behaviour resulting from the interaction of the objects, whose behaviour is defined through a set of simple rules that encode the individual behaviour and the transmission dynamic. METHODS: An epidemic is characterized trough an individual–based–model built upon cellular automata. In the proposed model, each individual of the population is represented by a cell of the automata. This way of modeling an epidemic situation allows to individually define the characteristic of each individual, establish different scenarios and implement control strategies. RESULTS: A cellular automata model to study the time evolution of a heterogeneous populations through the various stages of disease was proposed, allowing the inclusion of individual heterogeneity, geographical characteristics and social factors that determine the dynamic of the desease. Different assumptions made to built the classical model were evaluated, leading to following results: i) for low contact rate (like in quarantine process or low density population areas) the number of infective individuals is lower than other areas where the contact rate is higher, and ii) for different initial spacial distributions of infected individuals different epidemic dynamics are obtained due to its influence on the transition rate and the reproductive ratio of disease. CONCLUSIONS: The contact rate and spatial distributions have a central role in the spread of a disease. For low density populations the spread is very low and the number of infected individuals is lower than in highly populated areas. The spacial distribution of the population and the disease focus as well as the geographical characteristic of the area play a central role in the dynamics of the desease.
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spelling pubmed-40222362014-05-28 Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach López, Leonardo Burguerner, Germán Giovanini, Leonardo BMC Res Notes Research Article BACKGROUND: The spread of an infectious disease is determined by biological and social factors. Models based on cellular automata are adequate to describe such natural systems consisting of a massive collection of simple interacting objects. They characterize the time evolution of the global system as the emergent behaviour resulting from the interaction of the objects, whose behaviour is defined through a set of simple rules that encode the individual behaviour and the transmission dynamic. METHODS: An epidemic is characterized trough an individual–based–model built upon cellular automata. In the proposed model, each individual of the population is represented by a cell of the automata. This way of modeling an epidemic situation allows to individually define the characteristic of each individual, establish different scenarios and implement control strategies. RESULTS: A cellular automata model to study the time evolution of a heterogeneous populations through the various stages of disease was proposed, allowing the inclusion of individual heterogeneity, geographical characteristics and social factors that determine the dynamic of the desease. Different assumptions made to built the classical model were evaluated, leading to following results: i) for low contact rate (like in quarantine process or low density population areas) the number of infective individuals is lower than other areas where the contact rate is higher, and ii) for different initial spacial distributions of infected individuals different epidemic dynamics are obtained due to its influence on the transition rate and the reproductive ratio of disease. CONCLUSIONS: The contact rate and spatial distributions have a central role in the spread of a disease. For low density populations the spread is very low and the number of infected individuals is lower than in highly populated areas. The spacial distribution of the population and the disease focus as well as the geographical characteristic of the area play a central role in the dynamics of the desease. BioMed Central 2014-04-12 /pmc/articles/PMC4022236/ /pubmed/24725804 http://dx.doi.org/10.1186/1756-0500-7-234 Text en Copyright © 2014 López et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
López, Leonardo
Burguerner, Germán
Giovanini, Leonardo
Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach
title Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach
title_full Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach
title_fullStr Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach
title_full_unstemmed Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach
title_short Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach
title_sort addressing population heterogeneity and distribution in epidemics models using a cellular automata approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4022236/
https://www.ncbi.nlm.nih.gov/pubmed/24725804
http://dx.doi.org/10.1186/1756-0500-7-234
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