Modeling epidemics using cellular automata

White, Sara Hoya; Rey, Ángel Martín del; Sánchez, Gerardo Rodríguez

doi:10.1016/j.amc.2006.06.126

Cited by 171 publications

(120 citation statements)

References 14 publications

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“…Referências adicionais a tais abordagens são [17,16,36,11]. O enfoque do estudo é numérico-computacional e busca mostrar as diferenças significativas entre as soluções de modelos tipo SIR às distintas abordagens.…”

Section: Experimentos Computacionais E Resultadosunclassified

“…O modelo aqui discutido inspira-se em [36], em que os autores introduziram um modelo SIR para simular o espalhamento de epidemias e estratégia de vacinação. No artigo de [33] é apresentado um modelo espaço-temporal para descrever a dinâmica da população de vetores da doença de Chagas, dada a necessidade de estabelecer estratégias de controle, pois não existe vacina à doença.…”

Section: Modelagem Por Autômato Celular Tipo Difusivounclassified

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Autômatos Celulares e Sistema Multiagente para Simulação da Propagação de Doenças

Rizzi

Kaiser

et al. 2016

RITA

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Resumo: Neste trabalho é apresentado um estudo sobre modelos computacionais epidemiológicos enfocando o tipo Suscetível-Infectado-Removido, SIR, e diferentes estratégias de solução à simulação computacional da propagação de doenças transmissíveis. Mostra-se que modelos baseados em autômatos celulares tipo Lattice Gas Cellular Automata, LGCA, têm soluções assemelhadas às obtidas por sistemas multiagentes, diferentemente de autômatos celulares difusivos. Os resultados obtidos da literatura, bem como aqueles decorrentes deste trabalho, apontam que tais metodologias têm potencial à simulação da dinâmica de fenômenos ecológicos, biológicos e físicos que possam assim ser modelados. Indicam, contudo, que resultados mais condizentes com dados reais dependem do desenvolvimento e da parametrização de modelos que adicionem características essenciais ao fenômeno, como a interação entre indivíduos e meio ambiente, e a heterogeneidade do sistema relacional de contatos.

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Section: Experimentos Computacionais E Resultadosunclassified

Section: Modelagem Por Autômato Celular Tipo Difusivounclassified

Autômatos Celulares e Sistema Multiagente para Simulação da Propagação de Doenças

Rizzi

Kaiser

et al. 2016

RITA

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“…Have simulated epidemic disease spread using two-dimensional cellular automata. Similarly, White et al [18] proposed a model based on cellular automata for understanding epidemic spread. Although the study considers the effect of vaccination on the population, the model is not based on real parameters.…”

Section: Methodsmentioning

confidence: 99%

Simulating the Transmission and Recovery from Ebola Virus Disease (EVD) to Understand the Impact of Experimental Drug Might Have in Three West African Countries

Ghani¹

2017

J Healthc Commun

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Ebola Virus Disease (EVD) has become one of the deadly epidemics that have caused the death of many in recent times. Three West African countries; Liberia, Sierra Leone and Guinea have experienced the worst spread of the disease since the disease outbreak recently. There have been suspected, probable and confirmed 17,145 reported cases of the disease in all the affected countries, with 6,070 reported deaths. This paper employs the SIRS (Susceptible, Infected, Recovered and Susceptible) model and cellular automata model (global stochastic contact model) with real parameters that include average contact, recovery period, infectious period and transmission probability to simulate in order to understand how the new Ebola experimental drug (ZMAPP) that is used to cure 3 patients could impact on a population hit by the disease and the period of time it will take to eradicate the epidemic. The study can serve as a basis for understanding the vaccination effect on Ebola virus based on real parameters.

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“…The rich variety of CCA-and CA-based models that has been developed during the last decade for describing various spatial biological phenomena such as epidemics [6,14,28], population dynamics [3,5], tumor growth [13,23,24], biofilm development [21,22] and many other phenomena [10,25] is illustrative for the suitability of such models to mimic complex bioprocesses. In a forthcoming work, Baetens and De Baets [2] propose a generalized CCA for modelling various biological processes that are traditionally described by means of PDEs.…”

Section: The Modelmentioning

confidence: 99%

Tracking Uncertainty in a Spatially Explicit Susceptible-Infected Epidemic Model

Baetens

Baets

2010

Lecture Notes in Computer Science

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Abstract. In this paper we conceive an interval-valued continuous cellular automaton for describing the spatio-temporal dynamics of an epidemic, in which the magnitude of the initial outbreak and/or the epidemic properties are only imprecisely known. In contrast to well-established approaches that rely on probability distributions for keeping track of the uncertainty in spatio-temporal models, we resort to an interval representation of uncertainty. Such an approach lowers the amount of computing power that is needed to run model simulations, and reduces the need for data that are indispensable for constructing the probability distributions upon which other paradigms are based.

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Modeling epidemics using cellular automata

Cited by 171 publications

References 14 publications

Autômatos Celulares e Sistema Multiagente para Simulação da Propagação de Doenças

Autômatos Celulares e Sistema Multiagente para Simulação da Propagação de Doenças

Simulating the Transmission and Recovery from Ebola Virus Disease (EVD) to Understand the Impact of Experimental Drug Might Have in Three West African Countries

Tracking Uncertainty in a Spatially Explicit Susceptible-Infected Epidemic Model

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