{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,5]],"date-time":"2026-11-05T04:18:36Z","timestamp":1793852316074,"version":"4.6.1"},"reference-count":46,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T00:00:00Z","timestamp":1786060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1843782","type":"journal-article","created":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T05:28:26Z","timestamp":1786080506000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["From fragmented to integrated surveillance in LMICs: digital pathways for outbreak detection and vaccine intelligence"],"prefix":"10.3389","volume":"14","author":[{"given":"Delfin Lovelina","family":"Francis","sequence":"first","affiliation":[{"name":"Saveetha Dental College & Hospitals, Saveetha University, SIMATS","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saravanan Sampoornam Pape","family":"Reddy","sequence":"additional","affiliation":[{"name":"Department of Periodontology, Army Dental 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