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. 2022:32:101028.
doi: 10.1016/j.imu.2022.101028. Epub 2022 Aug 6.

Artificial neural network-based heuristic to solve COVID-19 model including government strategies and individual responses

Thongchai Botmart  1 , Zulqurnain Sabir  2 , Shumaila Javeed  3 , Rafaél Artidoro Sandoval Núñez  4 , Wajaree Weera  1 , Mohamed R Ali  5 , R Sadat  6
Affiliations

Artificial neural network-based heuristic to solve COVID-19 model including government strategies and individual responses

Thongchai Botmart et al. Inform Med Unlocked. 2022.

Abstract

The current work aims to design a computational framework based on artificial neural networks (ANNs) and the optimization procedures of global and local search approach to solve the nonlinear dynamics of the spread of COVID-19, i.e., the SEIR-NDC model. The combination of the Genetic algorithm (GA) and active-set approach (ASA), i.e., GA-ASA, works as a global-local search scheme to solve the SEIR-NDC model. An error-based fitness function is optimized through the hybrid combination of the GA-ASA by using the differential SEIR-NDC model and its initial conditions. The numerical performances of the SEIR-NDC nonlinear model are presented through the procedures of ANNs along with GA-ASA by taking ten neurons. The correctness of the designed scheme is observed by comparing the obtained results based on the SEIR-NDC model and the reference Adams method. The absolute error performances are performed in suitable ranges for each dynamic of the SEIR-NDC model. The statistical analysis is provided to authenticate the reliability of the proposed scheme. Moreover, performance indices graphs and convergence measures are provided to authenticate the exactness and constancy of the proposed stochastic scheme.

Keywords: Active-set; Artificial neural networks; Genetic algorithm; Nonlinear SEIR-NDC model; Spread of COVID-19.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
The proposed scheme GA-ASA structure for the SEIR-NDC system.
Fig. 2
Fig. 2
Best weight vectors for the nonlinear SEIR-NDC model.
Fig. 3
Fig. 3
Result comparison for the nonlinear SEIR-NDC model.
Fig. 4
Fig. 4
Best/Mean AE for the nonlinear SEIR-NDC model.
Fig. 5
Fig. 5
Performance measures for the nonlinear SEIR-NDC model.
Fig. 6
Fig. 6
Convergence of TIC, EVAF and MAD for the nonlinear SEIR-NDC model.
See this image and copyright information in PMC

References

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