Local travel behaviour under continuing COVID-19 waves– A proxy for pandemic fatigue?

Zhang, Nan; Hu, Tingrui; Shang, Shujia; Zhang, Shiyao; Jia, Wei; Chen, Jinhang; Zhang, Zixuan; Su, Bing; Wang, Zhenyu; Cheng, Reynold; Li, Yuguo

doi:10.1016/j.trip.2023.100757

Cited by 2 publications

(1 citation statement)

References 47 publications

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“…This enables tourism businesses to make informed decisions regarding pricing, marketing campaigns, resource allocation, and product development [7]. Furthermore, decision-making systems in the big data tourism industry utilize advanced algorithms and machine learning models to automate and streamline processes [8]. Predictive analytics can forecast future demand for specific destinations or services, allowing businesses to adjust inventory levels and pricing strategies accordingly.…”

Section: Introductionmentioning

confidence: 99%

Network Big Data Analysis with Multi-Factor Hashing Ethereum Classification (Mfh-Ec): An Approach to Develop Strategy of the Tourism Industry Under the Post-Epidemic Situation

Wang

2024

jes

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In the tourism industry, big data has emerged as a revolutionizing the way businesses operate and travelers experience destinations. With the vast amount of data generated from online bookings, social media interactions, and mobile applications, tourism companies can gain valuable insights into traveler preferences, behavior patterns, and market trends. This paper proposes a development strategy for the tourism industry in the post-epidemic situation, leveraging network big data analysis with Multi-Factor Hashing Ethereum Classification (MFH-EC). By harnessing the power of network big data, this strategy aims to provide insights into changing traveler preferences, market dynamics, and risk factors in the wake of the pandemic. The MFH-EC model facilitates the classification and analysis of diverse factors influencing tourism development, including economic indicators, health and safety measures, environmental conditions, and traveler sentiment. Through simulated experiments and empirical validations, the effectiveness of the proposed strategy is assessed, demonstrating significant improvements in predictive accuracy and decision-making capabilities. For instance, the MFH-EC model achieved an 80% accuracy rate in predicting tourism demand shifts and a 30% reduction in forecasting errors compared to traditional methods. These results underscore the potential of network big data analysis with MFH-EC in guiding strategic decision-making and fostering sustainable recovery and growth in the tourism industry post-epidemic.

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Section: Introductionmentioning

confidence: 99%

Network Big Data Analysis with Multi-Factor Hashing Ethereum Classification (Mfh-Ec): An Approach to Develop Strategy of the Tourism Industry Under the Post-Epidemic Situation

Wang

2024

jes

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Heterogeneous risk tolerance, in-groups, and epidemic waves

Tovissodé,

Baumgaertner

2024

Front. Appl. Math. Stat.

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There is a growing interest in the joint modeling of the dynamics of disease and health-related beliefs and attitudes, but coupling mechanisms are yet to be understood. We introduce a model where risk information, which can be delayed, comes in two flavors, including historical risk derived from perceived incidence data and predicted risk information. Our model also includes an interpretation domain where the behavioral response to risk information is subject to in-group pressure. We then simulate how the strength of behavioral reaction impacts epidemic severity as measured by epidemic peak size, number of waves, and final size. Simulated behavioral response is not effective when the level of protection that prophylactic behavior provides is as small as 50% or lower. At a higher level of 75% or more, we see the emergence of multiple epidemic waves. In addition, simulations show that different behavioral response profiles can lead to various epidemic outcomes that are non-monotonic with the strength of reaction to risk information. We also modeled heterogeneity in the response profile of a population and find they can lead to less severe epidemic outcome in terms of peak size.

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Local travel behaviour under continuing COVID-19 waves– A proxy for pandemic fatigue?

Cited by 2 publications

References 47 publications

Network Big Data Analysis with Multi-Factor Hashing Ethereum Classification (Mfh-Ec): An Approach to Develop Strategy of the Tourism Industry Under the Post-Epidemic Situation

Network Big Data Analysis with Multi-Factor Hashing Ethereum Classification (Mfh-Ec): An Approach to Develop Strategy of the Tourism Industry Under the Post-Epidemic Situation

Heterogeneous risk tolerance, in-groups, and epidemic waves

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