Surveillance of tuberculosis by analysing Google Trends

  • Luana Gorgueira Santos (Student)

Student thesis: Master's Thesis


Tuberculosis remains a global health concern, having caused around 1.5 million deathsin 2020. The Portuguese medical authorities are facing challenges to meet the goals of theWord Health Organization in the area of Tuberculosis. Early detection of potential Tubercu losis outbreaks is crucial for effective intervention and control, but traditional surveillancesystems often suffer from reporting lags and resource limitations, which were aggravatedby the COVID-19 pandemic. This thesis explores the potential of using Google Trends topredict Tuberculosis incidence in Portugal. Past research have shown promising results inthis area, suggesting that Google Trends search volume could complement existing surveil lance methods. To improve Tuberculosis surveillance system, we developed a syndromicapproach using 19 Tuberculosis-related terms extracted from Google Trends. Historicaldata on the incidence of Tuberculosis was extracted from the European Centre for DiseasePrevention and Control. After joining both datasets, we applied different machine learn ing models to forecast the monthly Tuberculosis incidence. Nextly, four accuracy metrics,including the Akaike Information Criterion, were used to select the best predictive model.Our empirical analysis shows that the forecast matches the seasonal patterns of Tubercu losis incidence in Portugal. While there are possible limitations that need to be addressedin future research, the surveillance system developed in this study might be a valuable toolfor public health authorities as it provides real-time information on potential new cases. Inthe long run, this system might help alleviate the burden of Tuberculosis and potentiallymitigate future outbreaks.
Date of Award5 Jul 2023
Original languageEnglish
Awarding Institution
  • Universidade Católica Portuguesa
SupervisorPedro Afonso Fernandes (Supervisor)


  • Tuberculosis surveillance
  • Google trends
  • Search volume
  • Machine learning
  • Accuracy metrics
  • Tuberculosis incidence forecasting
  • Portugal


  • Mestrado em Análise de Dados para Gestão

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