Modelling SIR-type epidemics by ODEs, PDEs, difference equations and cellular automata – A comparative study

Schneckenreither, Günter; Popper, Nikolas; Zauner, Günther; Breitenecker, Felix

doi:10.1016/j.simpat.2008.05.015

Cited by 37 publications

(32 citation statements)

References 3 publications

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“…The agent-base models can then be "cross-validated" by comparison with compartmental models or empirical databases. (For comparisons of SIRS and agent-based results, see: Connell et al (2009);Magal and Ruan (2014); Schneckenreither et al (2008); Skvortsov and Ristic (2012).) The detail available from agent-based models is fascinating and potentially much more useful than compartmental results, but such work is demanding.…”

Section: Discussionmentioning

confidence: 99%

Dynamics of an SIR model with vaccination dependent on past prevalence with high-order distributed delay

Eckalbar

2015

Biosystems

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Section: Discussionmentioning

confidence: 99%

Dynamics of an SIR model with vaccination dependent on past prevalence with high-order distributed delay

Eckalbar

2015

Biosystems

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“…Além de pesquisarem o efeito da distribuição espacial inicial não homogênea de infectados e discutirem estratégias de vacinação, os autores especificaram uma distribuição de campo médio para comparar o AC com tais resultados. Em [31], é apresentado um estudo comparativo entre diferentes métodos e abordagens, inclusive à dinâmica SIR, e dentre elas estão o LGCA e Stochastic Cellular Automata, SCA. Embora tais abordagens envolvam diferentes técnicas e tipos de interações espaciais, elas mostram resultados que são qualitativamente consistentes.…”

Section: Modelagem Por Autômato Celular Tipo Lgcaunclassified

“…Todos os modelos são conservativos para os parâmetros utilizados, em que s + i + r = 1 em todas simulações. São resultados assemelhados ao de [31], que estudaram modelos em autômatos celulares estocásticos, LGCA e baseado em equações diferenciais parciais.…”

Section: Comparação Entre Os Modelosunclassified

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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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“…All these properties are fitting to describe epidemiological spread. This explains why a significant number of epidemic models, especially probabilistic ones, have been based on this technique (Kawasaki et al, 2006;Schneckenreither et al, 2008;Sirakoulis et al, 2000;White et al, 2007). The simplicity of the concept of CA also results in simulations with short execution times (Emrich et al, 2008).…”

Section: Hybridization In Comparison With Single Paradigmsmentioning

confidence: 99%

Impact of the topology of metapopulations on the resurgence of epidemics rendered by a new multiscale hybrid modeling approach

Vincenot

Moriya

2011

Ecological Informatics

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Simulating epidemics in metapopulations is a challenging issue due to the large demographic and geographic scales to incorporate. Traditional epidemiologic models choose to simplify reality by ignoring both the spatial distribution of populations and possible intrapopulation heterogeneities, whereas more recent solutions based on Individual-Based Modeling (IBM) can achieve high precision but are costly to compute and analyze. We introduce here an original alternative to these two approaches, which relies on a novel hybrid modeling framework and incarnates a multiscale view of epidemics. The model relies on a technical fusion of two modeling paradigms: System Dynamics (SD) and Individual-Based Modeling. It features an aggregated representation of local outbreaks rendered in SD, and at the same time a spatially-explicit simulation of the spread between populations simulated in IBM. We first present the design of this deterministic model, show that it can reproduce the dynamics of real resurgent epidemics, and infer from the sensitivity of several spatial factors absent in compartmental models the importance of having large-scale epidemiological processes represented inside of an explicitly disaggregated metapopulation. After discussing the implications of results obtained from simulation runs and the applicability of this model, we conclude that SD-IB hybrid modeling can be an interesting choice to represent epidemics in a spatially-explicit way without necessarily taking into account individual heterogeneities, and therefore it can be considered as a valuable alternative to simple compartmental models suffering from detrimental effects of the well-mixed assumption.

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Modelling SIR-type epidemics by ODEs, PDEs, difference equations and cellular automata – A comparative study

Cited by 37 publications

References 3 publications

Dynamics of an SIR model with vaccination dependent on past prevalence with high-order distributed delay

Dynamics of an SIR model with vaccination dependent on past prevalence with high-order distributed delay

Autômatos Celulares e Sistema Multiagente para Simulação da Propagação de Doenças

Impact of the topology of metapopulations on the resurgence of epidemics rendered by a new multiscale hybrid modeling approach

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