Tourism Demand and the COVID-19 Pandemic: An LSTM Approach
Source of Publication
Tourism Recreation Research
© 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.
Taylor and Francis Ltd.
China, Coronavirus, deep learning, long short term memory, tourism development, USA
Polyzos, Stathis; Samitas, Aristeidis; and Spyridou, Anastasia Ef, "Tourism Demand and the COVID-19 Pandemic: An LSTM Approach" (2020). All Works. 3665.
Indexed in Scopus
Open Access Type
Bronze: This publication is openly available on the publisher’s website but without an open license