ArticleHealthcare informatics research2018
Google Search Trends Predicting Disease Outbreaks: An Analysis from India.
Article in Healthcare informatics research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 3 of them syntheses that pooled it.
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Who cites it
38 citing papers in PubMed, 3 syntheses or guidelines pooled it, 103 citations in OpenAlex.
- Association between public attention and monkeypox epidemic: A global lag-correlation analysis.Journal of medical virology · 2023Pooled it
- Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review.PLoS neglected tropical diseases · 2022Pooled it
- The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review.Journal of medical Internet research · 2020Pooled it
- Using Google Trends to Predict Dengue Outbreaks in Pakistan: A Time-Series and Causality Analysis.EJIFCC · 2026Article
- Evaluating Google Trends as a proxy for symptom incidence: insights from the winter COVID-19 infection study in England 2023/24.Epidemiology and infection · 2025Article
- Early Warning of Infectious Disease Outbreaks Using Social Media and Digital Data: A Scoping Review.International journal of environmental research and public health · 2025Article
- Investigating Google Trends to forecast acute febrile illness outbreaks in North India reported through the Integrated Disease Surveillance Program.BMC infectious diseases · 2025Article
- Advancing Early Warning Systems for Malaria: Progress, challenges, and future directions - A scoping review.PLOS global public health · 2025Article
- Evaluation of the Quality and Readability of Web-Based Information Regarding Foreign Bodies of the Ear, Nose, and Throat: Qualitative Content Analysis.JMIR formative research · 2024Article
- Measles and Pertussis outbreaks in England and Wales: a time-series analysis.NIHR open research · 2024Article
- Impact of asthma in Europe: A comparison of web search data in 21 European countries.The World Allergy Organization journal · 2023Article
- Using Google Trends to Estimate the Geographic Distribution of Soil-Transmitted Helminthiasis in the United States from 2016 to 2021.Tropical medicine and infectious disease · 2023Article
- Web-based surveillance of respiratory infection outbreaks: retrospective analysis of Italian COVID-19 epidemic waves using Google Trends.Frontiers in public health · 2023Article
- The Role of Heterogenous Real-world Data for Dengue Surveillance in Martinique: Observational Retrospective Study.JMIR public health and surveillance · 2022Observational
- Understanding Health Communication Through Google Trends and News Coverage for COVID-19: Multinational Study in Eight Countries.JMIR public health and surveillance · 2021Article
- Exploring the Utility of Google Mobility Data During the COVID-19 Pandemic in India: Digital Epidemiological Analysis.JMIR public health and surveillance · 2021Article
- Article
- Suitability of Google Trends™ for Digital Surveillance During Ongoing COVID-19 Epidemic: A Case Study from India.Disaster medicine and public health preparedness · 2021Article
- Evaluation of a Parsimonious COVID-19 Outbreak Prediction Model: Heuristic Modeling Approach Using Publicly Available Data Sets.Journal of medical Internet research · 2021Article
- Understanding urologic scientific publication patterns and general public interests on stone disease: lessons learned from big data platforms.World journal of urology · 2021Article
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesPrompt detection is a cornerstone in the control and prevention of infectious diseases. The Integrated Disease Surveillance Project of India identifies outbreaks, but it does not exactly predict outbreaks. This study was conducted to assess temporal correlation between Google Trends and Integrated Disease Surveillance Programme (IDSP) data and to determine the feasibility of using Google Trends for the prediction of outbreaks or epidemics.
methodsThe Google search queries related to malaria, dengue fever, chikungunya, and enteric fever for Chandigarh union territory and Haryana state of India in 2016 were extracted and compared with presumptive form data of the IDSP. Spearman correlation and scatter plots were used to depict the statistical relationship between the two datasets. Time trend plots were constructed to assess the correlation between Google search trends and disease notification under the IDSP.
resultsTemporal correlation was observed between the IDSP reporting and Google search trends. Time series analysis of the Google Trends showed strong correlation with the IDSP data with a lag of -2 to -3 weeks for chikungunya and dengue fever in Chandigarh (
conclusionsSimilar results were obtained when applying the results of previous studies to specific diseases, and it is considered that many other diseases should be studied at the national and sub-national levels.
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