Abstract
The integration of digital technologies has profoundly transformed the surveillance of infectious diseases over recent decades. The emergence of approaches grounded in digital health, genomic surveillance, health information systems, and data analytics opens new perspectives for measles and rubella surveillance. The present study provides a systematic mapping of the scientific literature on measles and rubella surveillance in the digital era, in order to characterize its evolution, identify emerging themes, and reveal its conceptual structure. A bibliometric analysis was conducted using the Scopus database, combining terms related to measles, rubella, epidemiological surveillance, and digital technologies. Only peer-reviewed articles published in English were retained. The analyses focused on the temporal evolution of publications, the identification of trend topics, the construction of thematic maps over two successive periods, and the conceptual structure of the corpus, examined through multiple correspondence analysis (MCA). All analyses were performed using the R package Bibliometrix. A total of 154 articles published between 1990 and 2026 were included. Scientific output increased progressively, particularly from 2010 onward, reaching a peak of 27 publications in 2025. Earlier research mainly focused on conventional surveillance, vaccination, and public health, whereas recent publications place greater emphasis on digital and genomic surveillance, electronic reporting systems, and data-driven analytical approaches. The terms machine learning, deep learning, and convolutional neural network are positioned within the quadrant of emerging themes. The MCA explained 86.11% of the cumulative variance, indicating that the first two dimensions adequately captured the conceptual structure of the corpus. Three major thematic clusters were identified: the epidemiological surveillance of communicable diseases, vaccination strategies, and the progressive integration of digital health systems. This study provides a systematic mapping of the scientific evolution of measles and rubella surveillance in the digital era. It underscores the growing prominence of genomic surveillance and digital health systems, as well as the need to deepen the integration of artificial intelligence (AI) methods to strengthen early outbreak detection, improve real-time vaccination monitoring, and support international elimination goals.