Towards an Effective Tuberculosis Surveillance in Indonesia through Google Trends - Fudholi, Dhomas Hatta; Fikri, Khairul (Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 2020)
Background. The search digital footprint, such as in Google Trend (GT), forms a large dataset that is suitable to be used as surveillance data and supports early warning systems. These advantages become great opportunities for disease surveillance agencies in Indonesia to get rapid early disease monitoring. Objective. Due to limited research in this area and the increasing level of internet penetration in Indonesia, a further study is needed in disease monitoring by utilizing Google Trends. In this research, we explore, analyze and create a set of the best search terms to be used in utilizing GT for disease surveillance in Indonesia, especially Tuberculosis. Method. We use correlation as the technique to define the relatedness between the real case data and GT results. We collect data from the Ministry of Health of Indonesia. From the data, we design a set of new search terms to take GT trend data. The collected data is analyzed using the Pearson correlation. Result. The analysis shows that the studied search terms give strong positive relationships between GT trend data and Tuberculosis cases number in Indonesia. From the correlation analysis, we get a set of proposed effective search terms with the highest score equals to 0.907. Conclusion. Finally, it is possible to monitor and make quick surveillance in tuberculosis in Indonesia through Google Trend and we have created a novel set of search terms that can be used as the basis in monitoring other diseases in Indonesia