Google Search Trends Predicting Disease Outbreaks: An Analysis from India

Area of Science:

• Epidemiology
• Public Health Surveillance
• Digital Epidemiology

Background:

• Prompt detection of infectious diseases is crucial for control and prevention.
• India's Integrated Disease Surveillance Project (IDSP) identifies outbreaks but lacks predictive capabilities.
• Utilizing digital data sources like Google Trends offers potential for enhanced disease surveillance.

Purpose of the Study:

• To assess the temporal correlation between Google Trends search queries and Integrated Disease Surveillance Programme (IDSP) data.
• To determine the feasibility of using Google Trends for predicting infectious disease outbreaks and epidemics in India.

Main Methods:

• Collected Google search query data for malaria, dengue fever, chikungunya, and enteric fever in Chandigarh and Haryana during 2016.
• Compared Google Trends data with IDSP presumptive case data using Spearman correlation and scatter plots.
• Analyzed time trends to evaluate the correlation between Google search trends and IDSP disease notifications.

Main Results:

• A temporal correlation was observed between IDSP reporting and Google Trends data.
• Google Trends demonstrated a strong correlation with IDSP data for chikungunya and dengue fever, with a lag of -2 to -3 weeks.
• Malaria and enteric fever also showed a moderate correlation with IDSP data, exhibiting a similar lag period.

Conclusions:

• Google Trends can serve as a valuable tool for predicting infectious disease outbreaks, showing significant correlation with established surveillance data.
• The findings suggest the potential for integrating digital data streams into national and sub-national disease surveillance systems.
• Further research is recommended to explore the application of Google Trends for a wider range of diseases and geographical areas.