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 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.