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

Abstract

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.

Assessment of using Google Trends for real-time monitoring of infectious disease outbreaks: a measles case study

“…Correlation analysis of measles between Google Trends search data and clinical case data was performed to evaluate if Google Trends search data are highly correlated with clinical case data, even for highly vaccinated diseases like measles. If so, then the same methods from the previous study 26 can be easily adapted to other diseases to establish the pseudo-surveillance system. The analysis was performed at the country level across 29 EU/EEA Member States and the UK.…”

“…The WHO has recommended the Moving Epidemic Method (MEM) as a tool for assessing the severity of epidemics 24 , 25 . We previously applied the MEM to Google Trends search data for respiratory syncytial virus (RSV) to demonstrate the feasibility of using Google Trends as a data source for real-time monitoring of RSV outbreaks 26 . This approach complements existing surveillance systems to monitor disease outbreaks in real-time, especially in countries with limited or no sentinel network surveillance.…”

A Large-Scale Dataset of Search Interests Related to Disease X Originating from Different Geographic Regions

“…Given the potential correlation between the volume of internet searches and the information needs of users [109,110], this dataset could be utilized by public health organizations at both local and global levels to obtain insights into the specific needs of the general public pertaining to Disease X in various geographic regions. The use of Google Trends as a supplementary tool to conventional surveillance systems for monitoring epidemics has proven to be effective, as demonstrated by multiple prior works in this field [111,112]. So, if an epidemic due to Disease X were to start, this dataset is expected to serve as a framework for public health organizations for the development of a surveillance system for Disease X.…”

Uso de Google Trends como herramienta infodemiológica en el campo de la Salud Laboral

“…El uso de Google Trends en el campo de la infodemiología ha sido empleado con éxito en numerosos estudios dentro del campo de la salud (5) , siendo especialmente útil para la detección precoz de brotes de enfermedades infecciosas, tales como gripes (7) , enfermedades de transmisión sexual (8)(9)(10) u otras enfermedades infecciosas (11)(12)(13) , incluso habiendo sido ya empleado para monitorizar los brotes de COVID-19 (14)(15)(16)(17) . Además, también se ha empleado satisfactoriamente para la detección de patrones de comportamiento en temas de nutrición y sobrepeso (18)(19)(20) , diabetes (21) , cuidado a domicilio (22) o enfermedad de la gota (23) entre otros.…”