An investigation of international tourist flow modelling during the pandemic

ABSTRACT

Infectious disease normally largely affects international tourist flow. For example, the novel coronavirus (COVID-19) outbreak has greatly affected global tourism, and its effect is expected to extend further. As the first country in the world to detect the pandemic, how the international tourist flow to Mainland China (hereafter known as China) changes along with the COVID-19 outbreak is still underexplored. To bridge this research gap, this study identifies the changes in the international tourist flows to China by categorizing international tourists to seven regions of the world and examines the degree of sensitivity among five clustered groups through Python modelling. Findings show that the international tourist flows from Europe, Asia Pacific and North America to China were largely affected by the COVID-19 outbreak. In addition, different sensitivity levels of the five identified clusters among the 193 countries affected by infectious disease ranged from ‘least sensitive’ to ‘most sensitive’. Practical implications are further discussed.

KEYWORDS:

• International tourism flow
• modelling
• pandemic

Acknowledgements

We would like to express our sincere thanks to Haoyu Kong, Yi Qi, and Anthony Pan for data processing. Prof. Rob Law participated at the early stage of this research when he was affiliated with the Hong Kong Polytechnic University.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China: [Grant Number 71673015]; Fundamental Research Funds for the Central Universities of Beijing Foreign Studies University: [Grant Number 2021JS001]; Ethnic Research Project of the National Committee of People's Republic of China: [Grant Number 2020-GMD-089].