Real-Time Monitoring of Infectious Disease Outbreaks with a Combination of Google Trends Search Results and the Moving Epidemic Method: A Respiratory Syncytial Virus Case Study.
Dawei Wang, Andrea Guerra, Frederick Wittke, John Cameron Lang, Kevin Bakker, Andrew W Lee, Lyn Finelli, Yao-Hsuan Chen
Author Information
Dawei Wang: Health Economic and Decision Sciences, Merck & Co., Inc., Kenilworth, NJ 07065, USA. ORCID
Andrea Guerra: Clinical Development, MSD, Kings Cross, London EC2M 6UR, UK.
Frederick Wittke: Clinical Development, MSD, CH-6005 Luzern, Switzerland.
John Cameron Lang: Health Economic and Decision Sciences, Merck & Co., Inc., Kenilworth, NJ 07065, USA.
Kevin Bakker: Health Economic and Decision Sciences, Merck & Co., Inc., Kenilworth, NJ 07065, USA.
Andrew W Lee: Clinical Development, Merck & Co., Inc., Kenilworth, NJ 07065, USA.
The COVID-19 pandemic has disrupted the seasonal patterns of several infectious diseases. Understanding when and where an outbreak may occur is vital for public health planning and response. We usually rely on well-functioning surveillance systems to monitor epidemic outbreaks. However, not all countries have a well-functioning surveillance system in place, or at least not for the pathogen in question. We utilized Google Trends search results for RSV-related keywords to identify outbreaks. We evaluated the strength of the Pearson correlation coefficient between clinical surveillance data and online search data and applied the Moving Epidemic Method (MEM) to identify country-specific epidemic thresholds. Additionally, we established pseudo-RSV surveillance systems, enabling internal stakeholders to obtain insights on the speed and risk of any emerging RSV outbreaks in countries with imprecise disease surveillance systems but with Google Trends data. Strong correlations between RSV clinical surveillance data and Google Trends search results from several countries were observed. In monitoring an upcoming RSV outbreak with MEM, data collected from both systems yielded similar estimates of country-specific epidemic thresholds, starting time, and duration. We demonstrate in this study the potential of monitoring disease outbreaks in real time and complement classical disease surveillance systems by leveraging online search data.
