Acute respiratory infections prediction through social media data and machine learning: a systematic review

Acute 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. This 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. The 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. The 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.