The profound impact of the coronavirus disease 2019 (COVID-19) pandemic on global tourism activity has rendered forecasts of tourism demand obsolete. Accordingly, scholars have begun to seek the best methods to predict the recovery of tourism from the devastating effects of COVID-19. In this study, econometric and judgmental methods were combined to forecast the possible paths to tourism recovery in Hong Kong. The autoregressive distributed lag-error correction model was used to generate baseline forecasts, and Delphi adjustments based on different recovery scenarios were performed to reflect different levels of severity in terms of the pandemic's influence. These forecasts were also used to evaluate the economic effects of the COVID-19 pandemic on the tourism industry in Hong Kong.

Search query data reflect users’ intentions, preferences and interests. The interest in using such data to forecast tourism demand has increased in recent years. The mixed data sampling (MIDAS) method is often used in such forecasting, but is not effective when moving average (MA) dynamics are involved. To investigate the relevance of the MA components in MIDAS models to tourism demand forecasting, an improved MIDAS model that integrates MIDAS and the seasonal autoregressive integrated moving average process is proposed. Its performance is tested by forecasting monthly tourist arrivals in Hong Kong from mainland China with daily composite indices constructed from a large number of search queries using the generalized dynamic factor model. The forecasting results suggest that this new model significantly outperforms the benchmark model. In addition, comparing the forecasts and nowcasts shows that the latter generally outperforms the former.

This study investigates whether pooling can improve the forecasting performance of tourism demand models. The short-term domestic tourism demand forecasts for 341 cities in China using panel data (pooled) models are compared with individual ordinary least squares (OLS) and naïve benchmark models. The pooled OLS model demonstrates much worse forecasting performance than the other models. This indicates the huge heterogeneity of tourism across cities in China. A marked improvement with the inclusion of fixed effects suggests that destination features that stay the same or vary very little over time can explain most of the heterogeneity. Adding spatial effects to the panel data models also increases forecasting accuracy, although the improvement is small. The spatial distribution of spillover effects is drawn on a map and a spatial pattern is recognized. Finally, when both spatial and temporal effects are taken into account, pooling improves forecasting performance.

Search query data have recently been used to forecast tourism demand. Linear models, particularly autoregressive integrated moving average with exogenous variable models, are often used to assess the predictive power of search query data. However, they are limited by their inability to model non-linearity due to their pre-assumed linear forms. Artificial neural network models could be used to model non-linearity, but mixed results indicate that their application is not appropriate in all situations. Therefore, this study proposes a new hybrid model that combines the linear and non-linear features of component models. The model outperforms other models when forecasting tourist arrivals in Hong Kong from mainland China, thus demonstrating the advantage of adopting hybrid models in forecasting tourism demand with search query data.

Background: The application of factor analysis in the study of the clinical symptoms of coronavirus disease 2019 was investigated, to provide a reference for basic research on COVID-19 and its prevention and control. Methods: The data of 60 patients with COVID-19 in Jingzhou Hospital of Traditional Chinese Medicine and the Second People's Hospital of Longgang District in Shenzhen were extracted using principal component analysis. Factor analysis was used to investigate the factors related to symptoms of COVID-19.Based on the combination of factors, the clinical types of the factors were defined according to our professional knowledge. Factor loadings were calculated, and pairwise correlation analysis of symptoms was performed.Results: Factor analysis showed that the clinical symptoms of COVID-19 cases could be divided into respiratory-digestive, neurological, cough-wheezing, upper respiratory, and digestive symptoms. Pairwise correlation analysis showed that there were a total of eight pairs of symptoms: fever-palpitation, coughexpectoration, expectoration-wheezing, dry mouth-bitter taste in the mouth, poor appetite-fatigue, fatiguedizziness, diarrhea-palpitation, and dizziness-headache. Conclusions:The symptoms and syndromes of COVID-19 are complex. Respiratory symptoms dominate, and digestive symptoms are also present. Factor analysis is suitable for studying the characteristics of the clinical symptoms of COVID-19, providing a new idea for the comprehensive analysis of clinical symptoms.