Analysis of a nonstandard computer method to simulate a nonlinear stochastic epidemiological model of coronavirus-like diseases

Macías‐Díaz, Jorge E.; Raza, Ali; Ahmed, Nauman; Rafiq, Muhammad

doi:10.1016/j.cmpb.2021.106054

Cited by 26 publications

(11 citation statements)

References 42 publications

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“…Mathematical modeling of infectious diseases is a mature area with many well-known methods [16] , [17] and there have been several efforts in modeling COVID-19 too [18] , [19] . Simple deterministic compartment models are a popular approach in simulating evolving pandemics [20] .…”

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

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“…Also, the spread of COVID-19 still continuous. In some papers, mathematical analysis revealing the spread of such a deathly disease have been presented [73][74][75].…”

Section: Applicationmentioning

confidence: 99%

Complex T-spherical fuzzy Dombi aggregation operators and their applications in multiple-criteria decision-making

Karaaslan

Dawood

2021

Complex Intell. Syst.

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Complex fuzzy (CF) sets (CFSs) have a significant role in modelling the problems involving two-dimensional information. Recently, the extensions of CFSs have gained the attention of researchers studying decision-making methods. The complex T-spherical fuzzy set (CTSFS) is an extension of the CFSs introduced in the last times. In this paper, we introduce the Dombi operations on CTSFSs. Based on Dombi operators, we define some aggregation operators, including complex T-spherical Dombi fuzzy weighted arithmetic averaging (CTSDFWAA) operator, complex T-spherical Dombi fuzzy weighted geometric averaging (CTSDFWGA) operator, complex T-spherical Dombi fuzzy ordered weighted arithmetic averaging (CTSDFOWAA) operator, complex T-spherical Dombi fuzzy ordered weighted geometric averaging (CTSDFOWGA) operator, and we obtain some of their properties. In addition, we develop a multi-criteria decision-making (MCDM) method under the CTSF environment and present an algorithm for the proposed method. To show the process of the proposed method, we present an example related to diagnosing the COVID-19. Besides this, we present a sensitivity analysis to reveal the advantages and restrictions of our method.

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“…An Euler method was used to discretize the model. The approach to discretizing models built from stochastic processes can use several techniques, for example, four-order Runge-Kutta, Euler Maruyama, and non-standard methods [11] . These methods have varying accuracy.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Bayesian Runge-Kutta method for dengue dynamic mapping

et al. 2023

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Analysis of a nonstandard computer method to simulate a nonlinear stochastic epidemiological model of coronavirus-like diseases

Cited by 26 publications

References 42 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

Complex T-spherical fuzzy Dombi aggregation operators and their applications in multiple-criteria decision-making

Stochastic Bayesian Runge-Kutta method for dengue dynamic mapping

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