TOURIST Study

A digital health study tracking travelers’ real-time risks

TOURIST: Tracking Health Outcomes in International Travelers

Launched: 2014 (TOURIST)
Follow-up Study: 2018–2024 (TOURIST2)
Institutions: University of Zurich, Swiss TPH, ETH Zurich
Lead Investigator: Andrea Farnham, Silja Buehler (TOURIST2)

Project Overview

The TOURIST study was the first prospective mHealth cohort study of international travelers, launched in 2014–2015. In a groundbreaking collaboration with the ETH Wearable Computer Lab, we developed a smartphone application that collected:

  • Passive data every 15 minutes: geolocation, temperature, weather conditions, and environmental context
  • Active data daily: traveler-reported health behaviors and symptoms
  • Clinical baseline data from the travel medicine consultation

Recruitment occurred at the travel clinics of the University of Zurich and University of Basel, with the study focused initially on travelers to Thailand.

Evolution: TOURIST2 Study

The success of the original TOURIST study led to the SNSF-funded TOURIST2 project, which significantly expanded the geographic scope and sample size:

  • Over 1,000 travelers tracked
  • Destinations: Brazil, India, Peru, Thailand, Tanzania, and China
  • Data streams: Health outcomes, GPS location, real-time environment, behavioral survey, travel clinic consultation
  • Duration: Daily follow-up for the full duration of each trip

The TOURIST2 study has yielded one of the richest digital health datasets available on traveler behavior and morbidity.

Key Innovations

  • 📱 Mobile-health surveillance: Real-time, ecological momentary data capture
  • 🧠 ReadyToGo algorithm: A pre-travel risk triage tool developed by University of Zurich was validated by the TOURIST dataset, showing that we can predict who is most at risk of health issues during travel
  • 🗺️ Country-specific risk profiling: Risk patterns vary dramatically by destination, requiring personalized guidance
  • ⚠️ Beyond infections: Non-communicable events—like injuries and accidents—are among the most common issues travelers face

Major Findings

  • ✈️ Risk prediction before departure is feasible using clinic data and pre-travel triage tools
  • 🤕 Accidents, injuries, and non-infectious illnesses are more frequent than expected—highlighting the need for expanded travel medicine guidelines
  • 🦟 Malaria exposure is rare: Most travelers avoid very high-risk areas despite visiting endemic countries
  • 🌎 Health risks differ by country, requiring destination-specific surveillance and counseling
  • 🔁 The dataset remains in use today, generating new insights into travel, behavior, and planetary health

Next Steps

  • 🧠 Continued machine learning analyses to refine predictive algorithms
  • 🧪 Integration of microbiome and AMR tracking into follow-up studies
  • 🧭 Application of the TOURIST platform to emerging global health threats and new traveler cohorts

Learn More

For data access, collaboration inquiries, or methodological documentation, please contact
📧 andrea.farnham@uzh.ch

📄 Publications from TOURIST and TOURIST2 are available on Google Scholar.

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