SynthesisBMC medical informatics and decision making2026
Acute respiratory infections prediction through social media data and machine learning: a systematic review.
Synthesis in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAcute respiratory infections (ARIs) represent a major global public health burden, requiring timely surveillance and early detection to mitigate their impact. Traditional epidemiological monitoring systems often suffer from reporting delays, motivating the exploration of alternative data sources such as social media combined with machine learning techniques.
methodsThis study presents a systematic review of the literature on ARI prediction using social media data and machine learning models. Relevant studies were identified through structured searches of major scientific databases following established systematic review guidelines, PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The selected studies were classified into four levels of complexity and subsequently analyzed in terms of data sources, feature extraction strategies, machine learning algorithms, evaluation metrics, and prediction objectives.
resultsThe reviewed studies demonstrate that social media platforms, particularly Twitter (now X), can provide valuable signals correlated with ARI incidence. A wide range of machine learning methods have been employed, including regression models, support vector machines, ensemble methods, and deep learning approaches. Overall, the results indicate that machine learning models leveraging social media data can achieve competitive predictive performance, often complementing or enhancing traditional surveillance systems. However, challenges related to data noise, population bias, and model generalization remain.
conclusionsThe findings highlight the potential of integrating social media data and machine learning techniques for ARI prediction and public health surveillance. While promising, future research should focus on improving data quality, model interpretability, and robustness, as well as on validating these approaches across different geographic regions and respiratory diseases.
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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.