SynthesisPloS one2023
Surveillance of communicable diseases using social media: A systematic review.
Synthesis in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Acute respiratory infections prediction through social media data and machine learning: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Innovating health prevention models in detecting infectious disease outbreaks through social media data: an umbrella review of the evidence.Frontiers in public health · 2024Pooled it
- Integrating mathematical modelling and artificial intelligence to combat emerging viral syndemics: A systematic review.New microbes and new infections · 2026Review
- Using Over-the-Counter Retail Medication Sales to Detect and Track Influenza-Like Illnesses Including Novel Diseases: Surveillance Modeling Study.JMIR public health and surveillance · 2026Article
- Open-source intelligence in public health: a decision-centred framework.European journal of public health · 2026Article
- Assessing social media platform data access for automated early warning of public health threats.European journal of public health · 2026Article
- Review
- Artificial intelligence in infection surveillance: Data integration, applications and future directions.Biomedical journal · 2026Review
- Cross-lingual transfer with multilingual language models for influenza-like-illness detection in social media texts.npj digital public health · 2026Article
- Privacy, Exploitation and Global Disease Surveillance: Can We Justly Prevent the Next Pandemic?Public health ethics · 2025Article
- Targeting COVID-19 and Human Resources for Health News Information Extraction: Algorithm Development and Validation.JMIR AI · 2024Article
- Innovations in public health surveillance: An overview of novel use of data and analytic methods.Canada communicable disease report = Releve des maladies transmissibles au Canada · 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
- Early warning system using primary health care data in the post-COVID-19 pandemic era: Brazil nationwide case-study.Cadernos de saude publica · 2024Article
- Sentiment analysis of epidemiological surveillance reports on COVID-19 in Greece using machine learning models.Frontiers in public health · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCommunicable diseases pose a severe threat to public health and economic growth. The traditional methods that are used for public health surveillance, however, involve many drawbacks, such as being labor intensive to operate and resulting in a lag between data collection and reporting. To effectively address the limitations of these traditional methods and to mitigate the adverse effects of these diseases, a proactive and real-time public health surveillance system is needed. Previous studies have indicated the usefulness of performing text mining on social media.
objectiveTo conduct a systematic review of the literature that used textual content published to social media for the purpose of the surveillance and prediction of communicable diseases. METHODOLOGY: Broad search queries were formulated and performed in four databases. Both journal articles and conference materials were included. The quality of the studies, operationalized as reliability and validity, was assessed. This qualitative systematic review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
resultsTwenty-three publications were included in this systematic review. All studies reported positive results for using textual social media content to surveille communicable diseases. Most studies used Twitter as a source for these data. Influenza was studied most frequently, while other communicable diseases received far less attention. Journal articles had a higher quality (reliability and validity) than conference papers. However, studies often failed to provide important information about procedures and implementation.
conclusionText mining of health-related content published on social media can serve as a novel and powerful tool for the automated, real-time, and remote monitoring of public health and for the surveillance and prediction of communicable diseases in particular. This tool can address limitations related to traditional surveillance methods, and it has the potential to supplement traditional methods for public health surveillance.
Indexed as
Identifiers
What OpenQuestion holds
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.