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

<ns3:p> Abstract* Background Tuberculosis (TB) is 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 cross-sectional design was adopted to analyze the relationship between search volumes for 53 TB-related terms on Google Trends and official Indonesia Health Profile data from 2019 to 2023 across 34 provinces. Case data were normalized (0–100) to reflect the Relative Search Volume (RSV). Statistical analysis was performed using the Spearman correlation test to assess the relationship between digital searches and actual cases. Results A consistently strong and positive correlation between TB search terms and case numbers across 34 Indonesian provinces (p &lt; 0.001). Key correlations included “Characteristics of Pulmonary TBC ( <ns3:italic>Ciri TBC paru</ns3:italic> )” (r = 0.722) and “Pulmonary TBC Medicine ( <ns3:italic>Obat TBC paru</ns3:italic> )” (r = 0.739) in 2019, “Characteristics of Pulmonary TB ( <ns3:italic>Ciri TB Paru</ns3:italic> )” and “TB Prevention ( <ns3:italic>Pencegahan TB</ns3:italic> )” (r = 0.704) in 2020, “Characteristics of Pulmonary TB ( <ns3:italic>Ciri TBC Paru</ns3:italic> )” (r = 0.731) and “Childhood Pulmonary TB ( <ns3:italic>TB Paru anak</ns3:italic> )” (r = 0.707) in 2021, “Pulmonary TB Drugs ( <ns3:italic>Obat TBC Paru</ns3:italic> )” (r = 0.782) and “Characteristics of Tuberculosis ( <ns3:italic>Ciri Tuberkulosis</ns3:italic> )” (r = 0.709) in 2022, as well as “Characteristics of Tuberculosis ( <ns3:italic>Ciri Tuberkulosis</ns3:italic> )” (r = 0.731) in 2023. Conclusion Google Trends data correlated strongly with official TB epidemiological data in Indonesia. These results suggest digital search trends can serve as complementary indicators to conventional surveillance and early warning systems. </ns3:p>

Authors

Institutions

Publication Details

Journal
F1000Research
Published
2026-04-28
DOI
https://doi.org/10.12688/f1000research.179772.1
Primary Topic
Data-Driven Disease Surveillance
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Aufiena Nur Ayu Merzistya, Slamet Rahayu, Chatila Maharani, Erli Widiastuti et al.
F1000Research
Data-Driven Disease Surveillance
article

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

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

Abstract

<ns3:p> Abstract* Background Tuberculosis (TB) is 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 cross-sectional design was adopted to analyze the relationship between search volumes for 53 TB-related terms on Google Trends and official Indonesia Health Profile data from 2019 to 2023 across 34 provinces. Case data were normalized (0–100) to reflect the Relative Search Volume (RSV). Statistical analysis was performed using the Spearman correlation test to assess the relationship between digital searches and actual cases. Results A consistently strong and positive correlation between TB search terms and case numbers across 34 Indonesian provinces (p &lt; 0.001). Key correlations included “Characteristics of Pulmonary TBC ( <ns3:italic>Ciri TBC paru</ns3:italic> )” (r = 0.722) and “Pulmonary TBC Medicine ( <ns3:italic>Obat TBC paru</ns3:italic> )” (r = 0.739) in 2019, “Characteristics of Pulmonary TB ( <ns3:italic>Ciri TB Paru</ns3:italic> )” and “TB Prevention ( <ns3:italic>Pencegahan TB</ns3:italic> )” (r = 0.704) in 2020, “Characteristics of Pulmonary TB ( <ns3:italic>Ciri TBC Paru</ns3:italic> )” (r = 0.731) and “Childhood Pulmonary TB ( <ns3:italic>TB Paru anak</ns3:italic> )” (r = 0.707) in 2021, “Pulmonary TB Drugs ( <ns3:italic>Obat TBC Paru</ns3:italic> )” (r = 0.782) and “Characteristics of Tuberculosis ( <ns3:italic>Ciri Tuberkulosis</ns3:italic> )” (r = 0.709) in 2022, as well as “Characteristics of Tuberculosis ( <ns3:italic>Ciri Tuberkulosis</ns3:italic> )” (r = 0.731) in 2023. Conclusion Google Trends data correlated strongly with official TB epidemiological data in Indonesia. These results suggest digital search trends can serve as complementary indicators to conventional surveillance and early warning systems. </ns3:p>

F1000ResearchVol. 15
State University of Semarang (ID), Universiti Teknologi MARA (MY)
Quality Education
Openalex Percentile: Top 6%
Data-Driven Disease Surveillance
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.