COVID-19 Tourism Recovery in the ASEAN and East Asia Region: Asymmetric Patterns and Implications

The aim of this paper is to produce forecasts for tourism flows and tourism revenue for ASEAN and East Asian countries after the end of the COVID-19 pandemic. By implementing two different machine-learning methodologies (the Long Short Term Memory neural network and the Generalised Additive Model) and using different training data sets, we aim to forecast the recovery patterns for these data series for the first 12 months after the end of crisis. We thus produce a baseline forecast, based on the averages of our different models, as well as a worst- and best-case scenario. We show that recovery is asymmetric across the group of countries in the ASEAN and East Asian region and that recovery in tourism revenue is generally slower than in tourist arrivals. We show significant losses of approximately 48%, persistent after 12 months, for some countries, while others display increases of approximately 40% when compared to pre-crisis levels. Our work aims to quantify the projected drop in tourist arrivals and tourism revenue for ASEAN and East Asian countries over the coming months. The results of the proposed research can be used by policymakers as they determine recovery plans, where tourism will undoubtedly play a very important role.