Numerical Study of Computer Virus Reaction Diffusion Epidemic Model

Fatima, Umbreen; Băleanu, Dumitru; Ahmed, Nauman; Azam, Shumaila; Raza, Ali; Rafiq, Muhammad; Rehman, M. A.

doi:10.32604/cmc.2021.012666

Cited by 12 publications

(6 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another study investigated the transmission dynamics of computer viruses interconnected via a global network. A numerical simulation example applied within the scope of the study confirmed the theoretical results of the designed technique [22]. In another study, a nonlinear delayed model was investigated to examine the dynamics of a virus in a computer network.…”

Section: Related Worksupporting

confidence: 58%

Novel Metrics for Mutation Analysis

Takan¹,

Katipoglu²

2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

A measure of the "goodness" or efficiency of the test suite is used to determine the proficiency of a test suite. The appropriateness of the test suite is determined through mutation analysis. Several Finite State Machine (FSM) mutants are produced in mutation analysis by injecting errors against hypotheses. These mutants serve as test subjects for the test suite (TS). The effectiveness of the test suite is proportional to the number of eliminated mutants. The most effective test suite is the one that removes the most significant number of mutants at the optimal time. It is difficult to determine the fault detection ratio of the system. Because it is difficult to identify the system's potential flaws precisely. In mutation testing, the Fault Detection Ratio (FDR) metric is currently used to express the adequacy of a test suite. However, there are some issues with this metric. If both test suites have the same defect detection rate, the smaller of the two tests is preferred. The test case (TC) is affected by the same issue. The smaller two test cases with identical performance are assumed to have superior performance. Another difficulty involves time. The performance of numerous vehicles claiming to have a perfect mutant capture time is problematic. Our study developed three metrics to address these issues: FDR TS j j , FDR TC j j , and FDR Time j j : In this context, most used test generation tools were examined and tested using the developed metrics. Thanks to the metrics we have developed, the research contributes to eliminating the problems related to performance measurement by integrating the missing parameters into the system.

show abstract

Section: Related Worksupporting

confidence: 58%

Novel Metrics for Mutation Analysis

Takan¹,

Katipoglu²

2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…The compartment model is expressed in an ordinary differential equation. This system explains that all individuals in the population can interact with the same possibilities [15] , [16] , [17] . However, individual interactions are heterogeneous.…”

Section: Related Workmentioning

confidence: 99%

Stochastic Bayesian Runge-Kutta method for dengue dynamic mapping

et al. 2023

View full text Add to dashboard Cite

“…To reduce the threat of virus spread, various strategies can be used using epidemic models. Many studies have been applied to the investigation of infectious diseases, including nonlinear differential equation models to describe nonlinear incidence rates and relative risk analysis [1][2][3][4]. Epidemic cases have played an important role as one of the causes of poverty, misery, and death for humanity for centuries.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Convolution for Stochastic Epidemic Model

Mukhsar¹,

Ahmar²,

Safty³

et al. 2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

Dengue Hemorrhagic Fever (DHF) is a tropical disease that always attacks densely populated urban communities. Some factors, such as environment, climate and mobility, have contributed to the spread of the disease. The Aedes aegypti mosquito is an agent of dengue virus in humans, and by inhibiting its life cycle it can reduce the spread of the dengue disease. Therefore, it is necessary to involve the dynamics of mosquito's life cycle in a model in order to obtain a reliable risk map for intervention. The aim of this study is to develop a stochastic convolution susceptible, infective, recovered-susceptible, infective (SIR-SI) model describing the dynamics of the relationship between humans and Aedes aegypti mosquitoes. This model involves temporal trend and uncertainty factors for both local and global heterogeneity. Bayesian approach was applied for the parameter estimation of the model. It has an intrinsic recurrent logic for Bayesian analysis by including prior distributions. We developed a numerical computation and carry out simulations in WinBUGS, an open-source software package to perform Markov chain Monte Carlo (MCMC) method for Bayesian models, for the complex systems of convolution SIR-SI model. We considered the monthly DHF data of the 2016-2018 periods from 10 districts in Kendari-Indonesia for the application as well as the validation of the developed model. The estimated parameters were updated through to Bayesian MCMC. The parameter estimation process reached convergence (or fulfilled the Markov chain properties) after 50000 burn-in and 10000 iterations. The deviance was obtained at 453.7, which is smaller compared to those in previous models. The districts of Wua-Wua and Kadia were consistent as high-risk areas of DHF. These two districts were considered to have a significant contribution to the fluctuation of DHF cases.

show abstract

Numerical Study of Computer Virus Reaction Diffusion Epidemic Model

Cited by 12 publications

References 24 publications

Novel Metrics for Mutation Analysis

Novel Metrics for Mutation Analysis

Stochastic Bayesian Runge-Kutta method for dengue dynamic mapping

Bayesian Convolution for Stochastic Epidemic Model

Contact Info

Product

Resources

About