Monitoring and Prediction of Infectious Diseases through Social Network Analysis - Wences, Pedro; ● Martínez, Alicia; Estrada, Hugo; Miranda, Sabino (Revista Mexicana de Ingenieria Biomedica, 2026)
This article presents a comprehensive approach for monitoring and predicting infectious diseases through social media analysis, focusing on the COVID-19 pandemic. The objective is to identify real-time contagion reports and estimate disease trends as a complement to traditional epidemiological surveillance. We developed a system integrating natural language processing techniques, employing the BERT model to classify contagion statements on X, and using the Gompertz function to forecast short-term case growth. The methodology also incorporates analysis of georeferenced posts, predictions via rolling windows, and spatial representation of risk areas through heat maps. Results indicate a significant correlation between X mentions and official health reports, suggesting temporal synchronicity between both data sources. Important limitations are acknowledged, such as the urban bias in X user samples and the underrepresentation of rural populations. Finally, it is concluded that social media represent a potentially valuable resource as a complementary source for generating timely epidemiological alerts, thereby strengthening public health decision-making.