A mathematical model of coronavirus transmission by using the heuristic computing neural networks

Sabir, Zulqurnain; Asmara, Adi; Dehraj, Sanaullah; Raja, Muhammad Asif Zahoor; Altamirano, Gilder Cieza; Salahshour, Soheil; Sadat, R.; Ali, Mohamed R.

doi:10.1016/j.enganabound.2022.10.033

Cited by 4 publications

(2 citation statements)

References 42 publications

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“…The following examples illustrate pertinent research studies. The study on the transmission of influenza in human populations utilizing the SEIR model encompasses an examination of the dissemination of the flu within human communities by employing SEIR models to forecast infection rates and evaluate the effectiveness of interventions, such as vaccination campaigns and quarantine measures [10][11][12].…”

Section: Related Workmentioning

confidence: 99%

“…The research demonstrates the effectiveness of utilizing the SEIR model as a valuable instrument for comprehending the intricacies associated with transmitting infectious illnesses within populations, facilitating informed decision-making during outbreaks and epidemics. Mathematical models, such as the SEIR model, have shown to be valuable tools for scientists and researchers in enhancing our comprehension of infectious illnesses and devising more efficient prevention and management methods [10,18,19]. Nevertheless, it is crucial to bear in mind that this model relies on specific assumptions, and hence, the outcomes should be cautiously interpreted within the framework of pertinent empirical evidence.…”

Section: Related Workmentioning

confidence: 99%

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Stability Analysis of Spread of Infectious Diseases COVID-19 Using SEIAR-V1V2Q Model for Asymptomatic Condition with Runge-Kutta Order 4

Erwin,

Hasibuan,

Siahaan

et al. 2024

MMEP

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The spread of infectious diseases in Indonesia has become a significant concern in health. COVID-19 contagious disease has difficulties in infection because some individuals are infected asymptomatically. Infectious diseases are modeled with a SEIR model modification with vaccinations 1 and 2, and Quarantine will produce a new approach by considering the variables and parameters of prevention so that it can suppress the rate of spread of the infectious disease COVID-19. The study will simulate a numerical analysis of the transmission model of contagious diseases solved using the Runge-Kutta order 4. The results achieved a new model with the SEIAR-V1V2Q modification, this model can predict the acceleration of the spread of the COVID-19 transmitted disease by considering asymptomatic conditions. Based on the research, modifying the SEIR model with 2-stage vaccination and quarantine measures can reduce the percentage of infection cases of susceptible individuals, especially cases of asymptomatic infection, which are cases of infected individuals without showing symptoms. Discipline in accelerating 2-stage vaccination will increase the formation of individual body immunity to strengthen unique antibodies to minimize infection with the COVID-19 virus. It can be a reference in similar cases requiring vaccination and Quarantine of infected individuals.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Stability Analysis of Spread of Infectious Diseases COVID-19 Using SEIAR-V1V2Q Model for Asymptomatic Condition with Runge-Kutta Order 4

Erwin,

Hasibuan,

Siahaan

et al. 2024

MMEP

View full text Add to dashboard Cite

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Viable intertwined supply network: Modelling and dynamic analysis using artificial neural networks

Bhat,

Aljuneidi

2025

Applied Soft Computing

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Reactor Temperature Prediction Method Based on CPSO-RBF-BP Neural Network

Tang

2023

Applied Sciences

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A neural network model based on a chaotic particle swarm optimization (CPSO) radial basis function-back propagation (RBF-BP) neural network was suggested to improve the accuracy of reactor temperature prediction. The training efficiency of the RBF-BP neural network is influenced to some degree by the large randomness of the initial weight and threshold. To address the impact of initial weight and threshold uncertainty on the training efficiency of the RBF-BP combined neural network, this paper proposes using a chaotic particle swarm optimization algorithm to correct the RBF-BP neural network’s initial weight and threshold, as well as to optimize the RBF-BP neural network to speed up the algorithm and improve prediction accuracy. The measured temperature of the reactor acquired by on-site enterprises was confirmed and compared to the predicted results of the BP, RBF-BP, and PSO-RBF-BP neural network models. Finally, Matlab simulation tests were performed, and the experimental data revealed that the CPSO-RBF-BP combined neural network model suggested in this paper had a root-mean-square error of 17.3%, an average absolute error of 11.4%, and a fitting value of 99.791%. Prediction accuracy and efficiency were superior to those of the BP, RBF-BP, and PSO-RBF-BP models. The suggested model’s validity and feasibility were established. The study findings may provide some reference values for the reactor’s temperature prediction.

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A mathematical model of coronavirus transmission by using the heuristic computing neural networks

Cited by 4 publications

References 42 publications

Stability Analysis of Spread of Infectious Diseases COVID-19 Using SEIAR-V1V2Q Model for Asymptomatic Condition with Runge-Kutta Order 4

Stability Analysis of Spread of Infectious Diseases COVID-19 Using SEIAR-V1V2Q Model for Asymptomatic Condition with Runge-Kutta Order 4

Viable intertwined supply network: Modelling and dynamic analysis using artificial neural networks

Reactor Temperature Prediction Method Based on CPSO-RBF-BP Neural Network

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