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Article
Tourism Demand and the COVID-19 Pandemic: An LSTM Approach
Tourism Recreation Research
  • Stathis Polyzos, Zayed University
  • Aristeidis Samitas, Zayed University
  • Anastasia Ef Spyridou, Gdańsk University of Technology
Document Type
Article
Publication Date
1-1-2020
Abstract

© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. This paper investigates the expected results of the current COVID-19 outbreak to arrivals of Chinese tourists to the USA and Australia. The growing market share of Chinese tourism and the fact that the county was the first to experience the pandemic make China a suitable proxy for predictions on global tourism. We employ data from the 2003 SARS outbreak to train a deep learning artificial neural network named Long Short Term Memory (LSTM). The neural network is calibrated for the particulars of the current pandemic. Our findings, which are cross-validated using backtesting, suggest that recovery of arrivals to pre-crisis levels can take from 6 to 12 months and this can have significant adverse effects not only on the tourism industry but also on other sectors that interact with it.

Publisher
Taylor and Francis Ltd.
Disciplines
Keywords
  • China,
  • Coronavirus,
  • deep learning,
  • long short term memory,
  • tourism development,
  • USA
Scopus ID
85087494570
Indexed in Scopus
Yes
Open Access
Yes
Open Access Type
Bronze: This publication is openly available on the publisher’s website but without an open license
https://doi.org/10.1080/02508281.2020.1777053
Citation Information
Stathis Polyzos, Aristeidis Samitas and Anastasia Ef Spyridou. "Tourism Demand and the COVID-19 Pandemic: An LSTM Approach" Tourism Recreation Research (2020) - 13 ISSN: <a href="https://v2.sherpa.ac.uk/id/publication/issn/0250-8281" target="_blank">0250-8281</a>
Available at: http://works.bepress.com/efstathios-polyzos/9/