ArticleScientific reports2019
The Assessment of Twitter's Potential for Outbreak Detection: Avian Influenza Case Study.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- When Infodemic Meets Epidemic: Systematic Literature Review.JMIR public health and surveillance · 2025Pooled it
- Internet-based Surveillance Systems and Infectious Diseases Prediction: An Updated Review of the Last 10 Years and Lessons from the COVID-19 Pandemic.Journal of epidemiology and global health · 2024Pooled it
- Digital Data Sources and Their Impact on People's Health: A Systematic Review of Systematic Reviews.Frontiers in public health · 2021Pooled it
- Analyzing Reddit Social Media Content in the United States Related to H5N1: Sentiment and Topic Modeling Study.Journal of medical Internet research · 2025Article
- Avian Influenza: Lessons from Past Outbreaks and an Inventory of Data Sources, Mathematical and AI Models, and Early Warning Systems for Forecasting and Hotspot Detection to Tackle Ongoing Outbreaks.Healthcare (Basel, Switzerland) · 2024Review
- Analysis of a European general wildlife health surveillance program: Chances, challenges and recommendations.PloS one · 2024Article
- Monitoring the Epidemiology of Otitis Using Free-Text Pediatric Medical Notes: A Deep Learning Approach.Journal of personalized medicine · 2023Article
- Forecasting virus outbreaks with social media data via neural ordinary differential equations.Scientific reports · 2023Article
- Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series Analysis.JMIR public health and surveillance · 2022Article
- An intelligent early warning system of analyzing Twitter data using machine learning on COVID-19 surveillance in the US.Expert systems with applications · 2022Article
- Understanding the vaccine stance of Italian tweets and addressing language changes through the COVID-19 pandemic: Development and validation of a machine learning model.Frontiers in public health · 2022Article
- Evaluation of Twitter data for an emerging crisis: an application to the first wave of COVID-19 in the UK.Scientific reports · 2021Article
- Pulse of the pandemic: Iterative topic filtering for clinical information extraction from social media.Journal of biomedical informatics · 2021Article
- Role of Participatory Health Informatics in Detecting and Managing Pandemics: Literature Review.Yearbook of medical informatics · 2021Review
- An analysis of COVID-19 vaccine sentiments and opinions on Twitter.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2021Article
- Social Media Data Analytics for Outbreak Risk Communication: Public Attention on the "New Normal" During the COVID-19 Pandemic in Indonesia.Computer methods and programs in biomedicine · 2021Article
- Visual analytics of COVID-19 dissemination in São Paulo state, Brazil.Journal of biomedical informatics · 2021Article
- Data and Digital Solutions to Support Surveillance Strategies in the Context of the COVID-19 Pandemic.Frontiers in digital health · 2021Review
- Examining risk and crisis communications of government agencies and stakeholders during early-stages of COVID-19 on Twitter.Computers in human behavior · 2021Article
- Track Iran's national COVID-19 response committee's major concerns using two-stage unsupervised topic modeling.International journal of medical informatics · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Social media services such as Twitter are valuable sources of information for surveillance systems. A digital syndromic surveillance system has several advantages including its ability to overcome the problem of time delay in traditional surveillance systems. Despite the progress made with using digital syndromic surveillance systems, the possibility of tracking avian influenza (AI) using online sources has not been fully explored. In this study, a Twitter-based data analysis framework was developed to automatically monitor avian influenza outbreaks in a real-time manner. The framework was implemented to find worrisome posts and alerting news on Twitter, filter irrelevant ones, and detect the onset of outbreaks in several countries. The system collected and analyzed over 209,000 posts discussing avian influenza on Twitter from July 2017 to November 2018. We examined the potential of Twitter data to represent the date, severity and virus type of official reports. Furthermore, we investigated whether filtering irrelevant tweets can positively impact the performance of the system. The proposed approach was empirically evaluated using a real-world outbreak-reporting source. We found that 75% of real-world outbreak notifications of AI were identifiable from Twitter. This shows the capability of the system to serve as a complementary approach to official AI reporting methods. Moreover, we observed that one-third of outbreak notifications were reported on Twitter earlier than official reports. This feature could augment traditional surveillance systems and provide a possibility of early detection of outbreaks. This study could potentially provide a first stepping stone for building digital disease outbreak warning systems to assist epidemiologists and animal health professionals in making relevant decisions.
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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.