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dc.contributor.authorThongchai Botmartes_ES
dc.contributor.authorZulqurnain Sabir bes_ES
dc.contributor.authorShumaila Javeed ces_ES
dc.contributor.authorRafaél Artidoro Sandoval Núñez des_ES
dc.contributor.authorWajaree weera aes_ES
dc.contributor.authorMohamed R. Ali ees_ES
dc.contributor.authorR. Sadat fes_ES
dc.description.abstractThe 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 orrectness 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.es_ES
dc.publisherInformatics in Medicine Unlockedes_ES
dc.relation.ispartofInformatics in Medicine Unlockedes_ES
dc.sourceInformatics in Medicine Unlocked 32 (2022) 101028es_ES
dc.subjectSpread of COVID-19es_ES
dc.subjectNonlinear SEIR-NDC modeles_ES
dc.subjectArtificial neural networkses_ES
dc.titleArtificial neural network-based heuristic to solve COVID-19 model including government strategies and individual responseses_ES

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