Public Search Behavior and Tuberculosis Cases in Indonesia 2019-2023: An Infodemiology Study Using Google Trends

Abstract Background Tuberculosis (TB) remains a major health challenge in Indonesia, which ranks second globally in 2024. As the 2030 elimination target approaches, gaps in early detection and public education persist. The public’s tendency to seek health information online before consulting professionals presents an opportunity to leverage infodemiology for public health surveillance. Therefore, this study aimed to assess the relationship between multi-term Google search trends and annual TB report data in Indonesia to disseminate the potential use of digital search data as a complementary indicator for epidemiological surveillance. Methods A repeated cross-sectional design was conducted using provincial data from all Indonesian provinces between 2019 and 2023. Official provincial TB case data were obtained from the Indonesian Health Profile, while Google Trends Relative Search Volume (RSV) data were extracted for 53 TB-related search terms. Provincial TB case counts were normalized to a 0–100 scale to match RSV values before analysis. Normality was assessed using the Shapiro–Wilk test. The associations between TB cases and RSV were examined using Spearman’s rank correlation. Multiple testing was controlled using the Benjamini–Hochberg False Discovery Rate (BH-FDR) procedure, with statistical significance defined as an FDR-adjusted p < 0.05. Results After false discovery rate adjustment, 38–42 of the 53 tuberculosis-related Google Trends search terms remained significantly correlated with monthly TB cases across 2019–2023, indicating a stable association between online search behavior and disease incidence. Colloquial search terms (e.g., “Flek Paru”), clinical characteristic queries, and keywords related to symptoms, prevention, transmission, treatment, and pediatric TB consistently showed the strongest positive correlations, with correlation coefficients reaching r = 0.731 (all adjusted p < 0.05). Conclusion Google Trends data, correlated strongly with TB case in Indonesia, can complement conventional TB surveillance by reflecting regional disease patterns and supporting timely monitoring, despite limitations related to internet access and search behavior.

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Journal
F1000Research
Published
2026-09-12
DOI
https://doi.org/10.12688/f1000research.179772.2
Primary Topic
Data-Driven Disease Surveillance
Type
article
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article

Public Search Behavior and Tuberculosis Cases in Indonesia 2019-2023: An Infodemiology Study Using Google Trends

Aufiena Nur Ayu Merzistya, Azliyana Azizan, Chatila Maharani, Sri Rahayu et al.
F1000Research
Data-Driven Disease Surveillance
article

Public Search Behavior and Tuberculosis Cases in Indonesia 2019-2023: An Infodemiology Study Using Google Trends

Aufiena Nur Ayu Merzistya, Azliyana Azizan, Chatila Maharani, Sri Rahayu, Erli Widiastuti, Deby Aulia Fandani, Aruna Daniswari, Salsabila Kinaya Pranindita, Widya Hary Cahyati, Amelia Saharani, Velia Nur Ardiyani, Erna Zuliana Muanifah
article en

Abstract

Abstract Background Tuberculosis (TB) remains a major health challenge in Indonesia, which ranks second globally in 2024. As the 2030 elimination target approaches, gaps in early detection and public education persist. The public’s tendency to seek health information online before consulting professionals presents an opportunity to leverage infodemiology for public health surveillance. Therefore, this study aimed to assess the relationship between multi-term Google search trends and annual TB report data in Indonesia to disseminate the potential use of digital search data as a complementary indicator for epidemiological surveillance. Methods A repeated cross-sectional design was conducted using provincial data from all Indonesian provinces between 2019 and 2023. Official provincial TB case data were obtained from the Indonesian Health Profile, while Google Trends Relative Search Volume (RSV) data were extracted for 53 TB-related search terms. Provincial TB case counts were normalized to a 0–100 scale to match RSV values before analysis. Normality was assessed using the Shapiro–Wilk test. The associations between TB cases and RSV were examined using Spearman’s rank correlation. Multiple testing was controlled using the Benjamini–Hochberg False Discovery Rate (BH-FDR) procedure, with statistical significance defined as an FDR-adjusted p < 0.05. Results After false discovery rate adjustment, 38–42 of the 53 tuberculosis-related Google Trends search terms remained significantly correlated with monthly TB cases across 2019–2023, indicating a stable association between online search behavior and disease incidence. Colloquial search terms (e.g., “Flek Paru”), clinical characteristic queries, and keywords related to symptoms, prevention, transmission, treatment, and pediatric TB consistently showed the strongest positive correlations, with correlation coefficients reaching r = 0.731 (all adjusted p < 0.05). Conclusion Google Trends data, correlated strongly with TB case in Indonesia, can complement conventional TB surveillance by reflecting regional disease patterns and supporting timely monitoring, despite limitations related to internet access and search behavior.

F1000ResearchVol. 15
State University of Semarang (ID), Universiti Teknologi MARA (MY)
Quality Education
Openalex Percentile: Top 10%
Data-Driven Disease Surveillance
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