Viral disease spreading in grouped population

Gwizdałła, Tomasz M.

doi:10.1016/j.cmpb.2020.105715

Cited by 13 publications

(9 citation statements)

References 28 publications

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

Section: Related Workunclassified

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

Kyriakou¹,

Georgoudas²,

Papanikolaou³

2022

Nat Comput

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In this study, we introduce an application of a Cellular Automata (CA)-based system for monitoring and estimating the spread of epidemics in real world, considering the example of a Greek city. The proposed system combines cellular structure and graph representation to approach the connections among the area’s parts more realistically. The original design of the model is attributed to a classical SIR (Susceptible–Infected–Recovered) mathematical model. Aiming to upgrade the application’s effectiveness, we have enriched the model with parameters that advances its functionality to become self-adjusting and more efficient of approaching real situations. Thus, disease-related parameters have been introduced, while human interventions such as vaccination have been represented in algorithmic manner. The model incorporates actual geographical data (GIS, geographic information system) to upgrade its response. A methodology that allows the representation of any area with given population distribution and geographical data in a graph associated with the corresponding CA model for epidemic simulation has been developed. To validate the efficient operation of the proposed model and methodology of data display, the city of Eleftheroupoli, in Eastern Macedonia—Thrace, Greece, was selected as a testing platform (Eleftheroupoli, Kavala). Tests have been performed at both macroscopic and microscopic levels, and the results confirmed the successful operation of the system and verified the correctness of the proposed methodology.

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

Section: Related Workunclassified

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

Kyriakou¹,

Georgoudas²,

Papanikolaou³

2022

Nat Comput

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“…Some of the particularities of COVID-19 are its high basic reproduction number [ 3 ]; a possible asymptomatic infectious period of up to 14 days, in which there is a group of individuals with no symptoms and the disease can be detected only through a serological test; and great variability in disease evolution and clinical outcomes [ 4 ]. It has been proven that the dynamics of this epidemic vary primarily according to the climate [ 5 ], local social and health habits [ [6] , [7] , [8] , [9] , [10] ], comorbidities [ 8 ] and age range [ 9 ], among other variables. The epidemiological analysis of the different outbreaks of this disease, which have been recorded worldwide, has helped develop different models, primarily mathematical, to track and anticipate the spread of epidemics [ [11] , [12] , [13] , [14] ].…”

Section: Introductionmentioning

confidence: 99%

City-scale model for COVID-19 epidemiology with mobility and social activities represented by a set of hidden Markov models

Pais

Godano

Juarez

et al. 2023

Computers in Biology and Medicine

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“…The Web of Science database reveals over 80,000 and over 110,000 papers related to this topic registered in 2020 and in January–November 2021, respectively, in contrast to only 19 papers in 2019. Among them, only several [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ] are based on the cellular automata technique [ 21 , 22 , 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Does Social Distancing Matter for Infectious Disease Propagation? An SEIR Model and Gompertz Law Based Cellular Automaton

Biernacki

Malarz

2022

Entropy

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In this paper, we present stochastic synchronous cellular automaton defined on a square lattice. The automaton rules are based on the SEIR (susceptible → exposed → infected → recovered) model with probabilistic parameters gathered from real-world data on human mortality and the characteristics of the SARS-CoV-2 disease. With computer simulations, we show the influence of the radius of the neighborhood on the number of infected and deceased agents in the artificial population. The increase in the radius of the neighborhood favors the spread of the pandemic. However, for a large range of interactions of exposed agents (who neither have symptoms of the disease nor have been diagnosed by appropriate tests), even isolation of infected agents cannot prevent successful disease propagation. This supports aggressive testing against disease as one of the useful strategies to prevent large peaks of infection in the spread of SARS-CoV-2-like diseases.

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Viral disease spreading in grouped population

Cited by 13 publications

References 28 publications

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

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

City-scale model for COVID-19 epidemiology with mobility and social activities represented by a set of hidden Markov models

Does Social Distancing Matter for Infectious Disease Propagation? An SEIR Model and Gompertz Law Based Cellular Automaton

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