Future behaviours decision-making regarding travel avoidance during COVID-19 outbreaks

Area of Science:

• Epidemiology
• Behavioral Science
• Data Science

Background:

• Human behavioral changes significantly impact epidemic forecasting, yet are poorly understood.
• Predicting behavioral shifts in response to disease outbreaks and interventions remains a challenge.
• Current methods lack the ability to observe decision-making processes underlying future behavior.

Purpose of the Study:

• To identify factors influencing behavioral changes for epidemic risk reduction.
• To demonstrate how accommodation reservation data can reveal decision-making processes.
• To quantitatively measure and predict population behavior for COVID-19 risk assessment.

Main Methods:

• Utilized accommodation reservation data as a proxy for travel avoidance behavior.
• Analyzed dynamic variations in travel avoidance motivation.
• Correlated behavioral changes with epidemic outbreak status and government interventions.

Main Results:

• Accommodation reservation data effectively reveals decision-making behind travel avoidance.
• The motivation for travel avoidance dynamically varied based on short-term future behaviors.
• Behavioral changes were significantly associated with COVID-19 outbreak status and/or government interventions.

Conclusions:

• Accommodation reservation data offers a novel method to understand and predict population behavior during epidemics.
• This approach enhances the understanding of behavioral responses to disease spread.
• Findings contribute to developing more reliable long-term epidemic forecasting models.