Modelling and simulating Chikungunya spread with an unstructured triangular cellular automata

Ortigoza, Gerardo; Brauer, Fred; Neri, Iris

doi:10.1016/j.idm.2019.12.005

Cited by 13 publications

(8 citation statements)

References 23 publications

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“…In Fig. 5, we observe the good agreement between our simulations and the Gaussian model given in (5). On the one side, the maximum number of infected is estimated as…”

Section: Active Brownian Particles and Sir Modelsupporting

confidence: 72%

“…Nevertheless, a more realistic model must consider the mobility of infectious particles and particle density within its environment. In this direction, self-propelled particles 2 , the random motion of non-interacting particles 3 , cellular automaton 4,5 , dynamical density functional theory approach 6 , and reaction-diffusion models 7,8 have been proposed to introduce the spatial motion of infectious particles. As a matter of universality, random diffusion models are intuitive and are extensively used to describe a wide range of biological processes ranging from bacteria motion to animal movement.…”

Section: Introductionmentioning

confidence: 99%

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Understanding Contagion Dynamics through Microscopic Processes in Active Brownian Particles

Norambuena,

Valencia,

Guzmán-Lastra

2020

Preprint

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Together with the universally recognized SIR model, several approaches have been employed to understand the contagious dynamics of interacting particles. Here, Active Brownian particles (ABP) are introduced to model the contagion dynamics of living agents that spread an infectious disease in space and time. Simulations were performed for several population densities and contagious rates. Our results show that ABP not only reproduces the time dependence observed in traditional SIR models, but also allows us to explore the critical densities, contagious radius, and random recovery times that facilitate the virus spread. Furthermore, we derive a first-principles analytical expression for the contagion rate in terms of microscopic parameters, without the assumption of free parameters as the classical SIR-based models. This approach offers a novel alternative to incorporate microscopic processes into the analysis of SIR-based models with applications in a wide range of biological systems.

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“…In Fig. 5, we observe the good agreement between our simulations and the Gaussian model given in (5). On the one side, the maximum number of infected is estimated as…”

Section: Active Brownian Particles and Sir Modelsupporting

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Understanding Contagion Dynamics through Microscopic Processes in Active Brownian Particles

Norambuena,

Valencia,

Guzmán-Lastra

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Nevertheless, a more realistic model must consider the mobility of infectious particles and particle density within its environment. In this direction, self-propelled particles 2 , 3 , the random motion of non-interacting particles 4 , 5 , cellular automaton 6 , 7 , dynamical density functional theory approach 8 , and reaction–diffusion models 9 , 10 have been proposed to introduce the spatial motion of infectious particles. As a matter of universality, active matter models are intuitive and are extensively used to describe a wide range of biological processes ranging from bacteria motion to animal movement 11 .…”

Section: Introductionmentioning

confidence: 99%

Understanding contagion dynamics through microscopic processes in active Brownian particles

Norambuena¹,

Valencia²,

Guzmán-Lastra³

2020

Sci Rep

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Together with the universally recognized SIR model, several approaches have been employed to understand the contagion dynamics of interacting particles. Here, Active Brownian particles (ABP) are introduced to model the contagion dynamics of living agents that perform a horizontal transmission of an infectious disease in space and time. By performing an ensemble average description of the ABP simulations, we statistically describe susceptible, infected, and recovered groups in terms of particle densities, activity, contagious rates, and random recovery times. Our results show that ABP reproduces the time dependence observed in traditional compartmental models such as the Susceptible-Infected-Recovery (SIR) models and allows us to explore the critical densities and the contagious radius that facilitates the virus spread. Furthermore, we derive a first-principles analytical expression for the contagion rate in terms of microscopic parameters, without considering free parameters as the classical SIR-based models. This approach offers a novel alternative to incorporate microscopic processes into analyzing SIR-based models with applications in a wide range of biological systems.

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“…Cellular automata (CA) are widely applied for modeling processes of virus spreading both on micro-level of cells of some organ [2] to macro-level of geo-spatial representation [3].…”

Section: Introductionmentioning

confidence: 99%

Modeling Ebola Virus Dynamics by Colored Petri Nets

Zaitsev

Ghaffari

Sanchez

2021

Proceedings of CECNet 2021

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We develop techniques to translate deterministic cellular automata models into colored Petri nets based on an example of known cellular automata for modeling and mimicking Ebola virus dynamics. Cellular automata for Ebola virus dynamics use parametric specifications of rules that is peculiarity of the present study. The simulation results completely coincide with known results obtained via dedicated simulator. Having uniform language for models specification brings in advantages for models mutual transformations and simulation.

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Modelling and simulating Chikungunya spread with an unstructured triangular cellular automata

Cited by 13 publications

References 23 publications

Understanding Contagion Dynamics through Microscopic Processes in Active Brownian Particles

Understanding Contagion Dynamics through Microscopic Processes in Active Brownian Particles

Understanding contagion dynamics through microscopic processes in active Brownian particles

Modeling Ebola Virus Dynamics by Colored Petri Nets

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