https://www.socictopen.socict.org/files/original/18e2331241ec8f3e5383fcb3012e746e.pdf d158154090246d53188755a30ddfcd84 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Determinants of Tourism Stocks During the COVID-19: Evidence From the Deep Learning Models Creator An entity primarily responsible for making the resource Wen-Tsao Pan, Qiu-Yu Huang, Zi-Yin Yang, Fei-Yan Zhu, Yu-Ning Pang, Mei-Er Zhuang Description An account of the resource This paper examines the determinants of tourism stock returns in China from October 25, 2018, to October 21, 2020, including the COVID-19 era. We propose four deep learning prediction models based on the Back Propagation Neural Network (BPNN): Quantum Swarm Intelligence Algorithms (QSIA), Quantum Step Fruit-Fly Optimization Algorithm (QSFOA), Quantum Particle Swarm Optimization Algorithm (QPSO) and Quantum Genetic Algorithm (QGA). Firstly, the rough dataset is used to reduce the dimension of the indices. Secondly, the number of neurons in the multilayer of BPNN is optimized by QSIA, QSFOA, QPSO, and QGA, respectively. Finally, the deep learning models are then used to establish prediction models with the best number of neurons under these three algorithms for the non-linear real stock returns. The results indicate that the QSFOA-BPNN model has the highest prediction accuracy among all models, and it is defined as the most effective feasible method. This evidence is robust to different sub-periods. Date A point or period of time associated with an event in the lifecycle of the resource 2021 Subject The topic of the resource deep learning, covid-19 era, Quantum genetic algorithm, back propagation neural network, Quantum particle swarm optimization algorithm, quantum step fruit fly optimization algorithm Identifier An unambiguous reference to the resource within a given context 10.3389/fpubh.2021.675801 Source A related resource from which the described resource is derived Epidemiology and Health Publisher An entity responsible for making the resource available Korean Society of Epidemiology Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Public aspects of medicine