ArticleTropical medicine and infectious disease2023
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.
Article in Tropical medicine and infectious disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Using Google Trends to Predict Dengue Outbreaks in Pakistan: A Time-Series and Causality Analysis.EJIFCC · 2026Article
- Applying the Moving Epidemic Method to Emergency Department Syndromic Surveillance to identify the Start of Influenza Seasons in New South Wales, Australia.Influenza and other respiratory viruses · 2026Article
- Public Search Behavior and Tuberculosis Cases in Indonesia 2019-2023: An Infodemiology Study Using Google Trends .F1000Research · 2026Article
- Use of the moving epidemic method to guide the launch of palivizumab immunization campaigns for respiratory syncytial virus in Québec, Canada.Canadian journal of public health = Revue canadienne de sante publique · 2025Article
- Early Warning of Infectious Disease Outbreaks Using Social Media and Digital Data: A Scoping Review.International journal of environmental research and public health · 2025Article
- Wastewater Surveillance for Group APathogens (Basel, Switzerland) · 2025Article
- Impact of Demographic and Clinical Subgroups in Google Trends Data: Infodemiology Case Study on Asthma Hospitalizations.Journal of medical Internet research · 2025Article
- Application of the moving epidemic method for influenza surveillance in Kunming.Frontiers in public health · 2025Article
- Assessment of using Google Trends for real-time monitoring of infectious disease outbreaks: a measles case study.Scientific reports · 2024Article
- Infodemiology of Influenza-like Illness: Utilizing Google Trends' Big Data for Epidemic Surveillance.Journal of clinical medicine · 2024Article
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Authors and funding
8 authors.
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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.
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