Analyzing the relationship between Google trends data and dengue outbreaks: a causality and correlation study
Abstract Objectives Dengue fever is a major public health problem in countries like India, where traditional surveillance systems often suffer from delays. The study aims to examine the relationship between Google Trends data and the official record of dengue outbreaks in India as a supplementary to...
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| Format: | Article |
| Language: | English |
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Springer
2025-06-01
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| Series: | Discover Public Health |
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| Online Access: | https://doi.org/10.1186/s12982-025-00725-0 |
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| author | Gaurav Singh Anupriya Jha M. K. Aadil |
| author_facet | Gaurav Singh Anupriya Jha M. K. Aadil |
| author_sort | Gaurav Singh |
| collection | DOAJ |
| description | Abstract Objectives Dengue fever is a major public health problem in countries like India, where traditional surveillance systems often suffer from delays. The study aims to examine the relationship between Google Trends data and the official record of dengue outbreaks in India as a supplementary tool to regular surveillance methods. Methods We used the Google Trends website to obtain the Google Trends data for the search terms “dengue fever,” “dengue symptoms,” and “dengue treatment” for the year 2023, along with the official record of the number of dengue outbreaks in the year 2023 from the Integrated Disease Surveillance Program (IDSP) website. Pearson’s correlation analysis, smoothed moving average, and the Toda-Yamamoto causality test were used to explore the strength, direction, and causality between the Google Trends data and official reports of the number of dengue outbreaks in India. Results The Toda-Yamamoto causality test revealed significant Granger causality between the search terms “dengue fever (p < 0.001),” “dengue symptoms (p < 0.001),” and “dengue treatment (p < 0.001)” with official records of the number of dengue outbreaks in India. Conclusion Google Trends data for the searched terms can supplement traditional surveillance methods for dengue outbreaks in India. Strong correlation coupled with significant Granger causality indicates its potential use as an early warning signal for dengue outbreaks in the country. |
| format | Article |
| id | doaj-art-e12534965f2d415eb344a545dfa439be |
| institution | Kabale University |
| issn | 3005-0774 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Public Health |
| spelling | doaj-art-e12534965f2d415eb344a545dfa439be2025-08-20T03:25:16ZengSpringerDiscover Public Health3005-07742025-06-0122111010.1186/s12982-025-00725-0Analyzing the relationship between Google trends data and dengue outbreaks: a causality and correlation studyGaurav Singh0Anupriya Jha1M. K. Aadil2All India Institute of Medical SciencesBalaji Institute of Medical SciencesAll India Institute of Medical SciencesAbstract Objectives Dengue fever is a major public health problem in countries like India, where traditional surveillance systems often suffer from delays. The study aims to examine the relationship between Google Trends data and the official record of dengue outbreaks in India as a supplementary tool to regular surveillance methods. Methods We used the Google Trends website to obtain the Google Trends data for the search terms “dengue fever,” “dengue symptoms,” and “dengue treatment” for the year 2023, along with the official record of the number of dengue outbreaks in the year 2023 from the Integrated Disease Surveillance Program (IDSP) website. Pearson’s correlation analysis, smoothed moving average, and the Toda-Yamamoto causality test were used to explore the strength, direction, and causality between the Google Trends data and official reports of the number of dengue outbreaks in India. Results The Toda-Yamamoto causality test revealed significant Granger causality between the search terms “dengue fever (p < 0.001),” “dengue symptoms (p < 0.001),” and “dengue treatment (p < 0.001)” with official records of the number of dengue outbreaks in India. Conclusion Google Trends data for the searched terms can supplement traditional surveillance methods for dengue outbreaks in India. Strong correlation coupled with significant Granger causality indicates its potential use as an early warning signal for dengue outbreaks in the country.https://doi.org/10.1186/s12982-025-00725-0DengueDisease outbreaksPublic health surveillanceTime series analysisCausality |
| spellingShingle | Gaurav Singh Anupriya Jha M. K. Aadil Analyzing the relationship between Google trends data and dengue outbreaks: a causality and correlation study Discover Public Health Dengue Disease outbreaks Public health surveillance Time series analysis Causality |
| title | Analyzing the relationship between Google trends data and dengue outbreaks: a causality and correlation study |
| title_full | Analyzing the relationship between Google trends data and dengue outbreaks: a causality and correlation study |
| title_fullStr | Analyzing the relationship between Google trends data and dengue outbreaks: a causality and correlation study |
| title_full_unstemmed | Analyzing the relationship between Google trends data and dengue outbreaks: a causality and correlation study |
| title_short | Analyzing the relationship between Google trends data and dengue outbreaks: a causality and correlation study |
| title_sort | analyzing the relationship between google trends data and dengue outbreaks a causality and correlation study |
| topic | Dengue Disease outbreaks Public health surveillance Time series analysis Causality |
| url | https://doi.org/10.1186/s12982-025-00725-0 |
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