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

Abstract

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.

A GIS-aided cellular automata system for monitoring and estimating graph-based spread of epidemics

“… 1997 ; Maniatty et al. 1999 ; Jithesh 2021 ; Gwizdalla 2020 ; Pereira et al. 2021 ; Bouaine and Rachik 2018 ; Blavatska and Holovatch 2021 ; Monteiro et al.…”

Analysis of Coronavirus Envelope Protein with Cellular Automata (CA) Model

“…Subsequently, Wolfram [39] proposed a simpler version of CA with two states per cell and three-neighbourhood. Research of a large number of authors from diverse disciplines has enriched the field of CA [40][41][42][43][44] and some researchers used CA to understand the dynamics of COVID-19 [45][46][47][48][49]. The book [44] covers a comprehensive survey of CA theory and wide varieties of applications.…”

“…There are 2 8 = 256 such rules for three-neighborhood CA. The decimal value derived out of 8 bit string of two CA rules (45) and (105) are illustrated in Table 1 Row 4 and 5. Eight different input combinations of a CA rule is a triplet of binary bits -111 (7), 110 (6), 101 (5), 100(4), 011(3), 010(2), 001 (1), 000(0), are noted on row 1 of Table 1.…”

Computational and Mathematical Methods in Medicine Prediction of COVID-19 in BRICS Countries: An Integrated Deep Learning Model of CEEMDAN-R-ILSTM-Elman

“…The results showed that the global COVID-19 pandemic network has special complex network characteristics. Jithesh [ 15 ] used the cellular automata which initially configured to have only susceptible and exposed states. Enlarged and evolved in discrete time steps to different infection states of the COVID-19 pandemic.…”