Can multi-source heterogeneous data improve the forecasting performance of tourist arrivals amid COVID-19? Mixed-data sampling approach.

The coronavirus disease (COVID-19) pandemic has already caused enormous damage to the global economy and various industries worldwide, especially the tourism industry. In the post-pandemic era, accurate tourism demand recovery forecasting is a vital requirement for a thriving tourism industry. Therefore, this study mainly focuses on forecasting tourist arrivals from mainland China to Hong Kong. A new direction in tourism demand recovery forecasting employs multi-source heterogeneous data comprising economy-related variables, search query data, and online news data to motivate the tourism destination forecasting system. The experimental results confirm that incorporating multi-source heterogeneous data can substantially strengthen the forecasting accuracy. Specifically, mixed data sampling (MIDAS) models with different data frequencies outperformed the benchmark models.

Historical pattern recognition with trajectory similarity for daily tourist arrivals forecasting

Forecasting daily tourist arrivals are crucial for tourism practitioners and researchers. Previous studies have shown that it is challenging to forecast the high volatility of daily tourist arrivals, especially during an emergency such as COVID-19. This study proposes a tourist arrival forecasting approach based on time series trajectory similarity (TS), which consists of five steps: (1) dividing the data into training sets, test sets, and matching sets;(2) using trajectory similarity to find the most similar historical time series within the current period;(3) data extraction, which uses the next day's data as a forecasting dataset by finding historically similar data;(4), (5) are the evaluation of forecasting methods and results, respectively. Based on the verification before and during COVID-19, the proposed approach has achieved excellent performance in forecasting daily tourist arrivals to Siguniang Mountain.

Forecasting Influenza Epidemics in Hong Kong Using Google Search Queries Data: A New Integrated Approach

Forecasting influenza epidemics has important practical implications. However, the performance of traditional methods adopting in Hong Kong influenza forecasting is limited due to its particularity. This paper proposes an integrated approach for Hong Kong influenza epidemics forecasting. The novelties of our approach mainly include: firstly, we adopt a model for Google search queries data collection and selection in Hong Kong to substitute Google Correlate. Secondly, we adopt the stacked autoencoder (SAE) to reduce the dimensionality of Google search queries data. Thirdly, we adopt a signal decomposition method named variational mode decomposition (VMD) to decompose the influenza data into modes with different frequencies, which can extract the characteristic. Fourthly, we use artificial neural networks (ANN) to forecast these modes of influenza epidemics extracted by VMD respectively, then these forecasts of each mode are added to generate the final forecasting results. From the perspective of forecasting accuracy and hypothesis tests, the empirical results show that our proposed integrated approach SAE-VMD-ANN significantly outperforms some other benchmark models both in the whole period and influenza season. The performance of our proposed model during the COVID-19 pandemic is checked too.

Containing the spread of coronavirus disease 2019 (COVID-19): Meteorological factors and control strategies.

The novel coronavirus disease 2019 (COVID-19) has spread globally and the meteorological factors vary greatly across the world. Understanding the effect of meteorological factors and control strategies on COVID-19 transmission is critical to contain the epidemic. Using individual-level data in mainland China, Hong Kong, and Singapore, and the number of confirmed cases in other regions, we explore the effect of temperature, relative humidity, and control measures on the spread of COVID-19. We find that high temperature mitigates the transmission of the disease. High relative humidity promotes COVID-19 transmission when temperature is low, but tends to reduce transmission when temperature is high. Implementing classical control measures can dramatically slow the spread of the disease. However, due to the occurrence of pre-symptomatic infections, the effect of the measures to shorten treatment time is markedly reduced and the importance of contact quarantine and social distancing increases.