ArticleComputer methods and programs in biomedicine2021
A model based on cellular automata for investigating the impact of lockdown, migration and vaccination on COVID-19 dynamics.
Article in Computer methods and programs in biomedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Model-free prognostication of non-linear time series.PloS one · 2026Article
- There exists the "smartest" movement rate to control the epidemic rather than "city lockdown".Applied mathematical modelling · 2022Article
- A symbiosis between cellular automata and dynamic weighted multigraph with application on virus spread modeling.Chaos, solitons, and fractals · 2022Article
- Computational and Mathematical Methods in Medicine Prediction of COVID-19 in BRICS Countries: An Integrated Deep Learning Model of CEEMDAN-R-ILSTM-Elman.Computational and mathematical methods in medicine · 2022Article
- A GIS-aided cellular automata system for monitoring and estimating graph-based spread of epidemics.Natural computing · 2022Article
- Simulation and forecasting models of COVID-19 taking into account spatio-temporal dynamic characteristics: A review.Frontiers in public health · 2022Review
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Abstract
BACKGROUND AND
objectiveCOVID-19 pandemic continues unabated due to the rapid spread of new mutant strains of the virus. Decentralized cluster containment is an efficient approach to manage the pandemic in the long term, without straining the healthcare system and economy. In this study, the objective is to forecast the peak and duration of COVID-19 spread in a cluster under different conditions, using a probabilistic cellular automata configuration designed to include the observed characteristics of the pandemic with appropriate neighbourhood schemes and transition rules.
methodsThe cellular automata, initially configured to have only susceptible and exposed states, enlarges and evolves in discrete time steps to different infection states of the COVID-19 pandemic. The transition rules take into account the probability and proximity of contact between infected hosts and susceptible individuals. A transmittable and transition neighbourhoods are defined to identify the most probable individuals infected from a single host in a time step.
resultsThe model with novel neighbourhood schemes and transition rules reproduce the macroscopic behaviour of infection and recovery observed in pandemics. The temporal evolution of the pandemic trajectory is sensitive to lattice size, range, latent and recovery periods but has constraints in capturing the changes in the infectious period. A study of lockdown and migration scenarios shows strict social isolation is crucial in controlling the pandemic. The simulations also indicate that earlier vaccination with a higher capacity and rate is essential to mitigate the pandemic. A comparison of simulated and actual data shows a good match.
conclusionsThe study concludes that social isolation during movement and interaction of people can limit the spread of new infections. Vaccinating a large proportion of the population reduces new cases in subsequent waves of the pandemic. The model and algorithm with real-world data as input can quickly forecast the trajectory of the pandemic, for effective response in cluster containment.
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