ArticleHealthcare informatics research2017
Methods Using Social Media and Search Queries to Predict Infectious Disease Outbreaks.
Article in Healthcare informatics research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Social media based surveillance systems for healthcare using machine learning: A systematic review.Journal of biomedical informatics · 2020Pooled it
- The Era of Preemptive Medicine: Developing Medical Digital Twins through Omics, IoT, and AI Integration.JMA journal · 2025Review
- Artificial intelligence-based prediction of pathogen emergence and evolution in the world of synthetic biology.Microbial biotechnology · 2024Review
- Association Between Internet Searches Related to Suicide/Self-harm and Adolescent Suicide Death in South Korea in 2016-2020: Secondary Data Analysis.Journal of medical Internet research · 2023Article
- The Association of Acute Watery Diarrhea in Iranian Pilgrims of Iraq with Cholera Cases in Iranians.Medical journal of the Islamic Republic of Iran · 2023Article
- Dynamic mechanism of eliminating COVID-19 vaccine hesitancy through web search.Frontiers in public health · 2023Article
- A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries.Life (Basel, Switzerland) · 2021Article
- Article
- Attention dynamics on the Chinese social media Sina Weibo during the COVID-19 pandemic.EPJ data science · 2021Article
- Data and Digital Solutions to Support Surveillance Strategies in the Context of the COVID-19 Pandemic.Frontiers in digital health · 2021Review
- Googling Musculoskeletal-Related Pain and Ranking of Medical Associations' Patient Information Pages: Google Ads Keyword Planner Analysis.Journal of medical Internet research · 2020Article
- Prediction of COVID-19 Outbreaks Using Google Trends in India: A Retrospective Analysis.Healthcare informatics research · 2020Article
- Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index.International journal of environmental research and public health · 2020Article
- Emojis in public health and how they might be used for hand hygiene and infection prevention and control.Antimicrobial resistance and infection control · 2020Review
- Correlation between Google Trends on dengue fever and national surveillance report in Indonesia.Global health action · 2019Article
- Modification of the Conventional Influenza Epidemic Models Using Environmental Parameters in Iran.Healthcare informatics research · 2019Article
- Article
- Vaccines Meet Big Data: State-of-the-Art and Future Prospects. From the Classical 3Is ("Isolate-Inactivate-Inject") Vaccinology 1.0 to Vaccinology 3.0, Vaccinomics, and Beyond: A Historical Overview.Frontiers in public health · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesFor earlier detection of infectious disease outbreaks, a digital syndromic surveillance system based on search queries or social media should be utilized. By using real-time data sources, a digital syndromic surveillance system can overcome the limitation of time-delay in traditional surveillance systems. Here, we introduce an approach to develop such a digital surveillance system.
methodsWe first explain how the statistics data of infectious diseases, such as influenza and Middle East Respiratory Syndrome (MERS) in Korea, can be collected for reference data. Then we also explain how search engine queries can be retrieved from Google Trends. Finally, we describe the implementation of the prediction model using lagged correlation, which can be calculated by the statistical packages, i.e., SPSS (Statistical Package for the Social Sciences).
resultsLag correlation analyses demonstrated that search engine data/Twitter have a significant temporal relationship with influenza and MERS data. Therefore, the proposed digital surveillance system can be used to predict infectious disease outbreaks earlier.
conclusionsThis prediction method could be the core engine for implementing a (near-) real-time digital surveillance system. A digital surveillance system that uses Internet resources has enormous potential to monitor disease outbreaks in the early phase.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.