ArticleExpert systems with applications2022
An intelligent early warning system of analyzing Twitter data using machine learning on COVID-19 surveillance in the US.
Article in Expert systems with applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
- Reviewing the progress of infectious disease early warning systems and planning for the future.BMC public health · 2024Pooled it
- Ensemble-labeling of infectious disease time series to evaluate early warning systems.Infectious Disease Modelling · 2026Article
- A kernelized data-driven approach for analyzing and predicting brucellosis in Inner Mongolia.Innovation (Cambridge (Mass.)) · 2026Article
- Artificial intelligence in infection surveillance: Data integration, applications and future directions.Biomedical journal · 2026Review
- Use of Large Language Models to Classify Epidemiological Characteristics in Synthetic and Real-World Social Media Posts About Conjunctivitis Outbreaks: Infodemiology Study.Journal of medical Internet research · 2025Article
- Geosocial Media's Early Warning Capabilities Across US County-Level Political Clusters: Observational Study.JMIR infodemiology · 2025Observational
- Identifying Medicine Shortages With the Twitter Social Network: Retrospective Observational Study.Journal of medical Internet research · 2024Observational
- Predicting Schistosomiasis Intensity in Africa: A Machine Learning Approach to Evaluate the Progress of WHO Roadmap 2030.The American journal of tropical medicine and hygiene · 2024Article
- Use of Large Language Models to Assess the Likelihood of Epidemics From the Content of Tweets: Infodemiology Study.Journal of medical Internet research · 2024Article
- Predicting the incidence of infectious diarrhea with symptom surveillance data using a stacking-based ensembled model.BMC infectious diseases · 2024Article
- Development of an early alert model for pandemic situations in Germany.Scientific reports · 2023Article
- A Twitter dataset for Monkeypox, May 2022.Data in brief · 2023Article
- Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives.Array (New York, N.Y.) · 2023Review
- COVID-19 CT ground-glass opacity segmentation based on attention mechanism threshold.Biomedical signal processing and control · 2023Article
- Deep-Learning Model for Influenza Prediction From Multisource Heterogeneous Data in a Megacity: Model Development and Evaluation.Journal of medical Internet research · 2023Article
- An overview of avian influenza surveillance strategies and modes.Science in One Health · 2023Review
- Developing an evidence-based TISM: an application for the success of COVID-19 Vaccination Drive.Annals of operations research · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The World Health Organization (WHO) declared on 11th March 2020 the spread of the coronavirus disease 2019 (COVID-19) a pandemic. The traditional infectious disease surveillance had failed to alert public health authorities to intervene in time and mitigate and control the COVID-19 before it became a pandemic. Compared with traditional public health surveillance, harnessing the rich data from social media, including Twitter, has been considered a useful tool and can overcome the limitations of the traditional surveillance system. This paper proposes an intelligent COVID-19 early warning system using Twitter data with novel machine learning methods. We use the natural language processing (NLP) pre-training technique, i.e., fine-tuning BERT as a Twitter classification method. Moreover, we implement a COVID-19 forecasting model through a Twitter-based linear regression model to detect early signs of the COVID-19 outbreak. Furthermore, we develop an expert system, an early warning web application based on the proposed methods. The experimental results suggest that it is feasible to use Twitter data to provide COVID-19 surveillance and prediction in the US to support health departments' decision-making.
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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.