An Infectious Disease Outbreak Simulator Based on the Cellular Automata Paradigm

Venkatachalam, Sangeeta; Mikler, Armin R.

doi:10.1007/11553762_20

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…They are dynamic simulation models defined by spatially arranged mathematical cells, which are updated in discrete steps according to a set of rules. The nature of these update rules determines whether the model will have a deterministic or a stochastic behaviour [15]. In recent years the development of CA models for the spread of infectious diseases has increased.…”

Section: David Publishingmentioning

confidence: 99%

Stochastic Lattice gas Cellular Automata Model for Epidemics

Gualtieri¹,

Hecht²

2016

JLS

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The aim of this study was to develop and explore a stochastic lattice gas cellular automata (LGCA) model for epidemics. A computer program was development in order to implement the model. An irregular grid of cells was used. A susceptible-infected-recovered (SIR) scheme was represented. Stochasticity was generated by Monte Carlo method. Dynamics of model was explored by numerical simulations. Model achieves to represent the typical SIR prevalence curve. Performed simulations also show how infection, mobility and distribution of infected individuals may influence the dynamics of propagation. This simple theoretical model might be a basis for developing more realistic designs.

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Section: David Publishingmentioning

confidence: 99%

Stochastic Lattice gas Cellular Automata Model for Epidemics

Gualtieri¹,

Hecht²

2016

JLS

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“…Cellular-automata offer an opportunity to study the effect of host spatial distribution on the dynamics of infectious disease transmission in a manner that captures the full complexity of the spatial structure of natural population distributions [15].…”

Section: Introductionmentioning

confidence: 99%

The Effect of Patchy Host Distribution on the Dynamics and Persistence of Directly-Transmitted Pathogens: A Cellular Automata Study

Kiszewski¹,

Awerbuch-Friedlander²

2013

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The effect of fragmented host distributions on the transmission dynamics of directly-transmitted pathogens was explored via stochastic automata simulation. Sixteen diverse population distributions varying in shape and density were used as a substrate for simulated outbreaks. Extended neighborhoods (80 cells), with probability of infection weighted by proximity to an infective source were used to define the overall probability of transitions from susceptible to infected. A static probability defined transitions from infected to recovered. The duration of active transmission as well as the proportion of each population infected per outbreak was averaged over a series of 30 simulations per parameter set. The level of aggregation for each population, measured in terms of the Moran Coefficient (MC) of spatial autocorrelation, was found to affect both the intensity of an outbreak and its length of persistence. Denser populations produced the most cases and lasted longer than those that were sparser. Elongated distributions, measured as the ratio between perimeter and area (PA) reversed some of the trends of increasing density. Long, narrow distributions produced fewer cases and were less persistent than populations composed of more compact clusters but with similar MC. Thus, both the shape and density of host distribution patterns affected the incidence rate, duration of epidemics and the percent of the population infected. Certain patterns of habitat fragmentation, thus, may put more hosts at risk of becoming infected than others.

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“…There exist a variety of approaches to the mathematical modelling of infectious diseases, with deterministic, stochastic and agent-based methods all having been used to study the spread of infection in a population [2,3,8,18,20,21,24,25,31,33]. The majority of these models are ordinary differential equations (that is, continuum) or Markov chain models, and ignore the spatial aspects of the epidemic process.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model for Studying Spatial Aspects of Infectious Diseases

2012

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We consider a hybrid model, created by coupling a continuum and an agent-based model of infectious disease. The framework of the hybrid model provides a mechanism to study the spread of infection at both the individual and population levels. This approach captures the stochastic spatial heterogeneity at the individual level, which is directly related to deterministic population level properties. This facilitates the study of spatial aspects of the epidemic process. A spatial analysis, involving counting the number of infectious agents in equally sized bins, reveals when the spatial domain is nonhomogeneous.2010 Mathematics subject classification: primary 37B15; secondary 92D30.

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An Infectious Disease Outbreak Simulator Based on the Cellular Automata Paradigm

Cited by 5 publications

References 10 publications

Stochastic Lattice gas Cellular Automata Model for Epidemics

Stochastic Lattice gas Cellular Automata Model for Epidemics

The Effect of Patchy Host Distribution on the Dynamics and Persistence of Directly-Transmitted Pathogens: A Cellular Automata Study

A Hybrid Model for Studying Spatial Aspects of Infectious Diseases

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