An analysis of influenza prevention measures from air travellers' perspective

A Novel Data Analytics Methodology for Discovering Behavioral Risk Profiles: The Case of Diners During a Pandemic

Understanding tourist profiles and behaviors during health pandemics is key to better preparedness for unforeseen future outbreaks, particularly for tourism and hospitality businesses. This study develops and applies a novel data analytics methodology to gain insights into the health risk reduction behavior of restaurant diners/patrons during their dining out experiences in a pandemic. The methodology builds on data relating to four constructs (question categories) and measurements (questions and attributes), with the constructs being worry, health risk prevention behavior, health risk reduction behavior, and demographic characteristics. As a unique contribution, the methodology generates a behavioral typology by identifying risk profiles, which are expressed as one- and two-level decision rules. For example, the results highlighted the significance of restaurants’ adherence to cautionary measures and diners’ perception of seclusion. These and other factors enable a multifaceted analysis, typology, and understanding of diners’ risk profiles, offering valuable guidance for developing managerial strategies and skill development programs to promote safer dining experiences during pandemics. Besides yielding novel types of insights through rules, another practical contribution of the research is the development of a public web-based analytics dashboard for interactive insight discovery and decision support.

Residents' Support for Tourism Amidst the COVID-19 Era: An Application of Social Amplification of Risk Framework and Knowledge, Attitudes, and Practices Theory

Given that the concept of risk perception stems primarily from consumer behaviour, tourism research has tended to address the issue from tourists' perspective, resulting in a lack of consideration of destination residents' risk perception and its impact on their attitudes and subsequent behaviour. Based on the social amplification of risk framework (SARF) and the knowledge, attitudes, and practices (KAP) theory, this study constructed a theoretical model to deepen the understanding of destination residents' support for tourism. Results indicate that residents' social media use, knowledge of COVID-19 and attitudes to tourism and tourists are all positively related to their support for tourism. Furthermore, residents' risk perception is negatively associated with their attitudes to tourism, attitudes to tourists and support for tourism. However, the relationship between residents' social media use and risk perception was not confirmed. Theoretical and managerial implications were discussed.

Epidemiological features and trends of influenza incidence in mainland China: A population-based surveillance study from 2005 to 2015

OBJECTIVES

To investigate epidemiological features and trends of influenza incidence with 1,173,640 cases in mainland China from 2005 to 2015.

METHODS

Incidence and mortality data for influenza from 2005 to 2015 were provided by the data-center of China public health science and covered a population of about 1.3 billion people from 31 provinces and regions in mainland China. Joinpoint regression and exploratory spatial data analyses were used to examine the incidence trends from 2005 to 2015.

RESULTS

The first upsurge in influenza cases occurred in 2009, and the highest incidence of influenza occurred in 2014 (15.9045 cases/100,000 people). The average incidence per year from 2009 to 2015 was threefold higher than that from 2005 to 2008 (10.5308 vs 3.4589 cases/100,000 people; incidence rate ratio=3.0446). The joinpoint regression results showed that there was an increasing influenza incidence trend from 2005 to 2015 (annual change in percentage=13.6%, 95%CI 2.2-26.3, p=0.0236). The seasonal pattern analysis showed that influenza typically occurred in winter and spring during each monitoring year, peaking from November to March the next year.

CONCLUSIONS

This study will help governments to make valuable decisions in allocating scarce resources and providing strategies to limit the spread of influenza.