Title

Tourism Demand and the COVID-19 Pandemic: An LSTM Approach

Document Type

Article

Source of Publication

Tourism Recreation Research

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.

ISSN

0250-8281

Publisher

Taylor and Francis Ltd.

Last Page

13

Disciplines

Business

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

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