Profiles of travelers to intermediate-high health risk areas following the reopening of borders in the COVID-19 crisis: A clustering approach.

Background: The reactivation of international travel in 2021 has created a new scenario in which the profile of the traveler to medium-high health risk areas may well have changed. However, few studies have analyzed this new profile since the reopening of borders in that year.

Methods: We designed an ad hoc questionnaire that was administered face-to-face by our medical team during appointments with 330 travelers in the second half of 2021. Information was collected on the following topics: sociodemographic and socioeconomic status; type of travel and previous travel experience; health status and risk perception (of COVID-19 and tropical infectious diseases). Using all features simultaneously, an unsupervised machine learning approach (k-means) is implemented to characterize groups of travelers. Pairwise chi-squared tests were performed to identify key features that showed statistically significant differences between clusters.

Results: The travelers were clustered into seven groups. We associated the clusters with different intensities of perceived risk of acquiring COVID-19 and tropical infectious diseases on the trip. The perceived risk of both diseases was low in the group "middle or lower middle class young inexperienced male tourist" but high in the group "middle or lower middle-class young with children inexperienced business traveler".

Conclusions: Broadening our knowledge of the profiles of travelers to intermediate-high health risk areas would help to tailor the health advice provided by practitioners to their characteristics and type of travel. In a changing health context, the k-means approach supposes a flexible statistical method that calculates travelers' profiles and can be easily adapted to process new information.