Cell-DEVS Models for the Spread of COVID-19

Cárdenas, Román; Henares, Kevin; Ruiz-Martín, Cristina; Wainer, Gabriel

doi:10.1007/978-3-030-69480-7_24

Cited by 8 publications

(18 citation statements)

References 14 publications

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“…This neighborhood definition allows cells to have unequal influences on one another, based on the shape of their 2D geographical borders. Ca ´rdenas et al 30 introduced a similar SIR model with unequal border lengths using the Cadmium Discrete-Event System Specification simulation library (the software used to implement the model presented in this research).…”

Section: Ca Pandemic Modelsmentioning

confidence: 99%

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A methodological approach for modeling the spread of disease using geographical discrete-event spatial models

et al. 2023

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The study of infectious disease models has become increasingly important during the COVID-19 pandemic. The forecasting of disease spread using mathematical models has become a common practice by public health authorities, assisting in creating policies to combat the spread of the virus. Common approaches to the modeling of infectious diseases include compartmental differential equations and cellular automata, both of which do not describe the spatial dynamics of disease spread over unique geographical regions. We introduce a new methodology for modeling disease spread within a pandemic using geographical models. We demonstrate how geography-based Cell-Discrete-Event Systems Specification (DEVS) and the Cadmium JavaScript Object Notation (JSON) library can be used to develop geographical cellular models. We exemplify the use of these methodologies by developing different versions of a compartmental model that considers geographical-level transmission dynamics (e.g. movement restriction or population disobedience to public health guidelines), the effect of asymptomatic population, and vaccination stages with a varying immunity rate. Our approach provides an easily adaptable framework that allows rapid prototyping and modifications. In addition, it offers deterministic predictions for any number of regions simulated simultaneously and can be easily adapted to unique geographical areas. While the baseline model has been calibrated using real data from Ontario, we can update and/or add different infection profiles as soon as new information about the spread of the disease become available.

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Section: Ca Pandemic Modelsmentioning

confidence: 99%

“…Ca ´rdenas et al 30 define an SEIRDS with more details in the infection profile, built using new simulation methodologies and tools. The model described here builds on this, including vaccinated states, and in turn, immunity rates, which affects the rate of exposures and thus infections.…”

Section: Ca Pandemic Modelsmentioning

confidence: 99%

A methodological approach for modeling the spread of disease using geographical discrete-event spatial models

et al. 2023

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“…The impact of migration and lockdown policies on the dynamics of the pandemic were also studied in this search [22]. Cárdenas et al [23] developed a system simulator named a Cell-DEVS to imitate and investigate the spread of disease, focusing on the COVID-19 pandemic. Cell-DEVS [24] combines CA with discrete event system specifications (DEVS) to describe ndimensional cell spaces as discrete-event models.…”

Section: The Comprehensive Theoretical Basismentioning

confidence: 99%

“…Cell-DEVS [24] combines CA with discrete event system specifications (DEVS) to describe ndimensional cell spaces as discrete-event models. Cárdenas et al [23] applied SIR and SIER models on the simulator. The simulation language was explained also in this work [23], [24].…”

Section: The Comprehensive Theoretical Basismentioning

confidence: 99%

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Computer modeling and simulation to predict COVID-19 propagation patterns via factual cellular automata

Al-Tabbakh

Karim

2022

IJEECS

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Computer modelling and simulation methods are very important and play a critical role in the mitigation and response to the ongoing COVID-19 pandemic. In this study, we propose a computational modeling technique based on Cellular Automata (CA) with realistic proposed rules. The rules are designed to simulate the propagation of COVID-19 disease through a bounded area. Our proposed CA rules are novel in many respects. For on, the classification of neighbors to nearest neighbors and range of neighbors based on cellular layers is explained. Moreover, the concepts of time generation and access time are deployed for the first time to model the propagation of the disease over time in this work. Further details of the proposed model including the topology of the defined area, the initial states of the cells and four-layer transfer mechanism are explained as well. This work may be considered a criterion of spreading for COVID-19 from point source in a defined population area. The results of the proposed algorithm represent the percentage of the population whose infectious status is described by different cellular state objects after a defined generation time. The results are compared under different circumstances and analyzed equanimity.

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