Use of unconventional data to monitor behaviors associated with influenza and dengue infections: a systematic review - Cuciniello, Rita; Ricciardi, Giovanni Emanuele; Ancona, Angela; Di Napoli, Davide; Tassan Din, Chiara; Castagna, Antonella; Renzi, Cristina; Rezza, Giovanni; Moro, Matteo; Chiecca, Greta (Annali di Igiene Medicina Preventiva e di Comunità, 2026)
Background: Influenza and dengue are two high-impact infectious diseases representing a persistent challenge to health systems worldwide. Their control depends not only on identification of viral transmission patterns, but also on understanding human choices, including vaccine uptake, personal protection, and adherence to public guidance. Classical reporting mechanisms, although reliable, often provide delayed or incomplete pictures of population behavior. In recent years, alternative information streams, such as online searches, social platforms, and mobile-based tools have been explored as rapid proxies to capture preventive actions and community sentiment. Methods: We performed a systematic review of the literature, according to PRISMA standards. Multiple databases (PubMed/MEDLINE, Scopus, EMBASE and PsycInfo) were queried without language restrictions. Eligible contributions were those employing unconventional digital traces or unconventional data to monitor prevention-related behaviors in the context of influenza or dengue. Extracted items included data source, infectious diseases explored, behavioral outcome and principal conclusions. Results: From 5,448 records, 44 articles satisfied inclusion parameters. Overall, 33 studies addressed influenza vaccination interest and protective measures, whereas nine examined dengue-related prevention behaviors, and two addressed both influenza and dengue. Internet search activity and microblogging platforms were the most frequently used sources. Approaches ranged from straightforward frequency tracking to advanced predictive algorithms. Several studies demonstrated that these data sources could anticipate behavioral shifts before official statistics; however, validation against ground-based behavioral measures was inconsistent and representativeness remained a recurrent concern. Discussion: Unconventional information streams appear promising for complementing established monitoring frameworks by offering faster signals and broader contextual awareness. Yet their usefulness is tempered by biases in digital participation, susceptibility to rumor propagation, and lack of standardized evaluation. Considering the distinct characteristics of influenza and dengue, these findings suggest that unconventional data can enrich prevention monitoring if integrated with traditional systems, coupled with rigorous methodological assessment, and applied with attention to equity.