An Investigation of the Effects of Lifting Quarantine Conditions after the Peak of COVID-19 Pandemic Using the Cellular Automata Approach

Ghaemi, Mehrdad

doi:10.21203/rs.3.rs-31778/v1

Cited by 1 publication

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References 3 publications

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“…CA configurations, with their flexibility for defining heterogeneous states and local rules for state transitions based on neighbourhood information, are ideal for simulating epidemics [31] , [32] , [33] . This method has been successfully used for predicting long term behaviour of COVID-19 [34] and also to understand the effect of lockdown measures in a region [35] , [36] .…”

Section: Introductionmentioning

confidence: 99%

A model based on cellular automata for investigating the impact of lockdown, migration and vaccination on COVID-19 dynamics

Jithesh

2021

Computer Methods and Programs in Biomedicine

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B ackground and Objective COVID-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. M ethods The 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. R esults The 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. C onclusions The 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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Section: Introductionmentioning

confidence: 99%

A model based on cellular automata for investigating the impact of lockdown, migration and vaccination on COVID-19 dynamics

Jithesh

2021

Computer Methods and Programs in Biomedicine

View full text Add to dashboard Cite

show abstract

An Investigation of the Effects of Lifting Quarantine Conditions after the Peak of COVID-19 Pandemic Using the Cellular Automata Approach

Cited by 1 publication

References 3 publications

A model based on cellular automata for investigating the impact of lockdown, migration and vaccination on COVID-19 dynamics

A model based on cellular automata for investigating the impact of lockdown, migration and vaccination on COVID-19 dynamics

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