From fragmented to integrated surveillance in LMICs: digital pathways for outbreak detection and vaccine intelligence.

BACKGROUND: Low- and middle-income countries (LMICs) often rely on fragmented, disease-specific surveillance systems that produce delayed and incomplete data. Recent outbreaks, including COVID-19, have highlighted the urgent need for integrated, real-time digital surveillance to improve early outbreak detection and optimize vaccine deployment. METHODS: A structured narrative review was conducted following SANRA recommendations on the literature published between 2019 and 2025 on digital health, artificial intelligence (AI), genomic surveillance, and vaccine-information systems in LMIC infectious disease surveillance. PubMed, Scopus, WHO IRIS, and regional CDC repositories were searched using pre-specified Boolean strings. Over 312 records were screened; 46 unique references were selected for final synthesis. RESULTS: Traditional indicator-based systems in LMICs suffer from siloed reporting, poor connectivity, and workforce shortages. Digital platforms (DHIS2, mobile reporting, cloud dashboards) can unify multi-sector data and accelerate outbreak signals. AI tools offer predictive capabilities, though LMIC-specific external validation remains limited in only 14% of published models. Electronic immunization registries demonstrate measurable improvements: stockout reductions of up to 76%, coverage gains of 12.3%, and median reporting delays cut from 28 to 3 days. Key barriers include non-standardized data formats, intermittent connectivity, algorithmic bias, and data governance gaps. CONCLUSIONS: Achieving integrated surveillance in LMICs requires adherence to interoperability standards, robust digital infrastructure, One Health data integration, and equitable data governance. A four-layer conceptual framework is presented. Investment in local capacity, supportive policy, and ethical AI frameworks is critical for sustainable innovation.