The aim of this paper is to study the effects of the spread of the COVID-19 virus in different regions and its impact on the economy and regional tourist flows. To this end, the researchers have been guided by a set of propositions which they have tried to demonstrate with the results obtained. This research shows that the impact of the pandemic is still being evaluated. The analysis of the relationship between the tourism sector and the pandemic outbreak in Spain provides an instructive case study to assist tourism in its recovery process. The paper delves into the impacts on the main Spanish touristic regions during the pandemic and providing implications for tourism recovery. In Spain, the tourism sector is of major economic importance, becoming one of the most vulnerable countries when crisis affects this industry. The negative image of the country due to the high infection rates has had a negative impact on travel and tourism. The Balearic Islands have been the most affected region with an 87% decrease in tourist visitors. The trips made by Spanish residents inside the Spanish territory shows the first increase found in the series analyzed. Domestic tourism not only represents an opportunity for all regions in this critical situation, but the types of accommodation also play a key role.

The application of machine learning techniques in the field of tourism is experiencing a remarkable growth, as they allow to propose efficient solutions to problems present in this sector, by means of an intelligent analysis of data in their specific context. The increase of work in this field requires an exhaustive analysis through a quantitative approach of research activity, contributing to a deeper understanding of the progress of this field. Thus, different approaches in the field of tourism will be analyzed, such as planning, forecasting, recommendation, prevention, and security, among others. As a result of this analysis, among other findings, the greater impact of supervised learning in the field of tourism, and more specifically those techniques based on neural networks, has been confirmed. The results of this study would allow researchers not only to have the most up‐to‐date and accurate overview of the application of machine learning in tourism, but also to identify the most appropriate techniques to apply to their domain of interest, as well as other similar approaches with which to compare their own solutions.This article is categorized under: Application Areas > Society and Culture Technologies > Machine Learning Application Areas > Business and Industry

Unsustainable models of governance belonging to a widespread neoliberal mindset in developed countries have commonly been applied in the tourism industry. The management of the COVID-19 pandemic crisis has provided exemplary lessons regarding the application of sustainable models of governance. Through a participatory research, guidances are provided to tackle the COVID-19 effects in the tourist sector, namely in the Spanish southwestern region of Sierra de Gata. Seventeen indicators are proposed to enhance the safety measures, commitment of tourist authorities, communities empowered and protection of common resources among tourism industry, tourist authority and communities to spread cooperative awareness, mutual trust and shared objectives. Using a sample of 161 tourism companies, we tested a model of tourism governance with two focus groups during May and October 2020. Structural equation modelling (SEM) was utilized. Based on the data attained from a questionnaire and interviews, a sustainable tourism model to recover the threatened tourism sector is proposed. Indeed, our results can be used to draw theoretical and practical conclusions such as 1.) connecting private and public interactions to tackle the spread of the virus and strategies to recover the damaged tourist sector, 2.) to develop corporative values among the tourist industry and communities, 3.) to enhance governance models (trusts, consortia, tourist boards, clusters) to promote cooperation, 4.) to improve the local participation of companies, communities and associations in decision-making, and 5.) to prioritize qualitative development goals over quantitative ones, in the touristic territory. These conclusions are applicable to other regions suffering from the damaging consequences of the pandemic.

The health crisis caused by the outbreak of the COVID-19 disease has devastated the worldwide hospitality sector. The current situation has led many countries to implement drastic rules to stop the spread of the virus. According to the Spanish health authority decisions need to be made in the context of uncertainty and lack of knowledgeable experiences through a gradual and asymmetric de-escalation process planned in four phases. Although the vast majority of studies refer to economic risks and impacts on tourist flows and economic income, few of them explicitly investigates safety and health measures that hotel managers should implement to their customers. Over a population of 12,740 hotels, 823 Spanish hotel managers have been involved in a participatory study. With the aim of assessing the actions taken to stop the spread of the virus, empirical research was implemented. A model presented four variables and 13 indicators which have been previously tested among hotel managers in the tourism sector. Five conclusions are drawn from the hypotheses: (1) Mass testing surveillance in customers and employees should be quick, affordable, and homogeneous throughout the European Union. (2) Training measures need to be taken by both public authorities and the private sector to reach a knowledgeable crisis management team with high commitment to the customer’s health and safety. (3) Protocols established by public authorities should be observed and adjusted gradually not only in hotels but also in tourist arrivals. (4) Healthy measures need to be periodically updated. (5) Each hotel should set up a surveillance process to guarantee the safety to their customers.