ArticleScientific reports2016
High correlation of Middle East respiratory syndrome spread with Google search and Twitter trends in Korea.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 1 of them a synthesis that pooled it.
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Who cites it
60 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review.Journal of medical Internet research · 2018Pooled it
- Surveillance of Twitter Data on COVID-19 Symptoms During the Omicron Variant Period: A Sentiment Analysis.JMIR formative research · 2025Article
- Analysis of Social Media Perceptions During the COVID-19 Pandemic in the United Kingdom: Social Listening Study (2019-2022).JMIR formative research · 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
- Utilizing Google Trends data to enhance forecasts and monitor long COVID prevalence.Communications medicine · 2025Article
- Discovering Time-Varying Public Interest for COVID-19 Case Prediction in South Korea Using Search Engine Queries: Infodemiology Study.Journal of medical Internet research · 2024Article
- Public interest trends for COVID-19 and pandemic trajectory: A time-series analysis of US state-level data.PLOS digital health · 2024Article
- Development of an early alert model for pandemic situations in Germany.Scientific reports · 2023Article
- Enhancing the Predictive Power of Google Trends Data Through Network Analysis: Infodemiology Study of COVID-19.JMIR public health and surveillance · 2023Article
- Evolving Face Mask Guidance During a Pandemic and Potential Harm to Public Perception: Infodemiology Study of Sentiment and Emotion on Twitter.Journal of medical Internet research · 2023Article
- Web-based surveillance of respiratory infection outbreaks: retrospective analysis of Italian COVID-19 epidemic waves using Google Trends.Frontiers in public health · 2023Article
- Forecasting the COVID-19 vaccine uptake rate: an infodemiological study in the US.Human vaccines & immunotherapeutics · 2022Article
- Monitoring real-time transmission heterogeneity from incidence data.PLoS computational biology · 2022Article
- Spatial and sentiment analysis of public opinion toward COVID-19 pandemic using twitter data: At the early stage of vaccination.International journal of disaster risk reduction : IJDRR · 2022Article
- Forecasting and Surveillance of COVID-19 Spread Using Google Trends: Literature Review.International journal of environmental research and public health · 2022Review
- Public Interest and Accessibility of Telehealth in Japan: Retrospective Analysis Using Google Trends and National Surveillance.JMIR formative research · 2022Article
- Seasonal Variation for Plantar Fasciitis: Evidence from Google Trends Search Query Data.Healthcare (Basel, Switzerland) · 2022Article
- An optimal control policy in fighting COVID-19 and infectious diseases.Applied soft computing · 2022Article
- The national burden of scabies in Germany: a population-based approach using Internet search engine data.Infection · 2022Article
- How COVID-19 Has Influenced Public Interest in Antimicrobials, Antimicrobial Resistance and Related Preventive Measures: A Google Trends Analysis of Italian Data.Antibiotics (Basel, Switzerland) · 2022Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Middle East respiratory syndrome coronavirus (MERS-CoV) was exported to Korea in 2015, resulting in a threat to neighboring nations. We evaluated the possibility of using a digital surveillance system based on web searches and social media data to monitor this MERS outbreak. We collected the number of daily laboratory-confirmed MERS cases and quarantined cases from May 11, 2015 to June 26, 2015 using the Korean government MERS portal. The daily trends observed via Google search and Twitter during the same time period were also ascertained using Google Trends and Topsy. Correlations among the data were then examined using Spearman correlation analysis. We found high correlations (>0.7) between Google search and Twitter results and the number of confirmed MERS cases for the previous three days using only four simple keywords: "MERS", "" ("MERS (in Korean)"), "" ("MERS symptoms (in Korean)"), and "" ("MERS hospital (in Korean)"). Additionally, we found high correlations between the Google search and Twitter results and the number of quarantined cases using the above keywords. This study demonstrates the possibility of using a digital surveillance system to monitor the outbreak of MERS.
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