ArticlePloS one2016
Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 3 of them syntheses that pooled it.
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Who cites it
42 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Pooled it
- Social media based surveillance systems for healthcare using machine learning: A systematic review.Journal of biomedical informatics · 2020Pooled it
- The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review.Journal of medical Internet research · 2020Pooled it
- Building Graduate Capabilities Through a Course-Wide Public Health and Health Promotion ECHA AI Curriculum Framework.Health promotion journal of Australia : official journal of Australian Association of Health Promotion Professionals · 2026Article
- Forecasting tuberculosis in Ethiopia using deep learning: progress toward sustainable development goal evidence from global burden of disease 1990-2021.BMC infectious diseases · 2025Article
- Use of Digital Tools in Arbovirus Surveillance: Scoping Review.Journal of medical Internet research · 2024Article
- Challenges in Media Attention toward COVID-19-Preventive Behaviors: Dual Roles of Threat and Perceived Capability for Information Systems and Health Care.Behavioral sciences (Basel, Switzerland) · 2024Article
- The role of social media in public health crises caused by infectious disease: a scoping review.BMJ global health · 2023Article
- Forecasting influenza hospital admissions within English sub-regions using hierarchical generalised additive models.Communications medicine · 2023Article
- Leveraging machine learning approaches for predicting potential Lyme disease cases and incidence rates in the United States using Twitter.BMC medical informatics and decision making · 2023Article
- Using geospatial social media data for infectious disease studies: a systematic review.International journal of digital earth · 2023Article
- The Aspects of Running Artificial Intelligence in Emergency Care; a Scoping Review.Archives of academic emergency medicine · 2023Article
- Influenza surveillance systems using traditional and alternative sources of data: A scoping review.Influenza and other respiratory viruses · 2022Article
- Social media effectiveness as a humanitarian response to mitigate influenza epidemic and COVID-19 pandemic.Annals of operations research · 2022Article
- Ethical Perspectives in Sharing Digital Data for Public Health Surveillance Before and Shortly After the Onset of the COVID-19 Pandemic.Ethics & behavior · 2022Article
- Twitter Research Synthesis for Health Promotion: A Bibliometric Analysis.Iranian journal of public health · 2021Article
- Public attitudes toward COVID-19 vaccines on English-language Twitter: A sentiment analysis.Vaccine · 2021Article
- Role of Participatory Health Informatics in Detecting and Managing Pandemics: Literature Review.Yearbook of medical informatics · 2021Review
- The Use of Social Media for Health Research Purposes: Scoping Review.Journal of medical Internet research · 2021Article
- Effective Training Data Extraction Method to Improve Influenza Outbreak Prediction from Online News Articles: Deep Learning Model Study.JMIR 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
Traditional methods for monitoring influenza are haphazard and lack fine-grained details regarding the spatial and temporal dynamics of outbreaks. Twitter gives researchers and public health officials an opportunity to examine the spread of influenza in real-time and at multiple geographical scales. In this paper, we introduce an improved framework for monitoring influenza outbreaks using the social media platform Twitter. Relying upon techniques from geographic information science (GIS) and data mining, Twitter messages were collected, filtered, and analyzed for the thirty most populated cities in the United States during the 2013-2014 flu season. The results of this procedure are compared with national, regional, and local flu outbreak reports, revealing a statistically significant correlation between the two data sources. The main contribution of this paper is to introduce a comprehensive data mining process that enhances previous attempts to accurately identify tweets related to influenza. Additionally, geographical information systems allow us to target, filter, and normalize Twitter messages.
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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.