{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T23:15:47Z","timestamp":1787613347248,"version":"build-2736575974"},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T00:00:00Z","timestamp":1750982400000},"content-version":"vor","delay-in-days":56,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,5,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>Measles continues to pose a serious threat to global public health, fueled by declining vaccination rates, international travel, and persistent immunization gaps. Early outbreak detection and response remain hampered by fragmented surveillance systems, which often lack interoperability and limit data accessibility.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>To address the major limitations of current measles surveillance systems\u2014including data fragmentation and lack of standardization\u2014we developed Measles Tracker, an integrated near-real-time data hub that centralizes and harmonizes measles surveillance data in the United States using publicly available sources. The system aggregates data from multiple layers, including: (1) official reports from public health agencies, (2) epidemiological surveillance bulletins, and (3) outbreak reports, mainly captured through news websites or via news aggregators. The platform architecture implements (1) geospatial normalization of key epidemiological variables (case counts, vaccination coverage, age-stratified incidence) and (2) dynamic visualization interfaces to support coordination of evidence-based response.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Measles Tracker enhances situational awareness by integrating disparate data streams in near real-time, enabling rapid geospatial detection of outbreak clusters, mapping vaccination gaps, and supporting dynamic risk stratification of vulnerable populations. It is intended exclusively as a complementary tool to official public health systems, providing educational and situational awareness without interfering with contact tracing, vaccination, or outbreak control activities.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>As a centralized, scalable tool, Measles Tracker advances measles surveillance by leveraging digital epidemiology principles. Future iterations will incorporate additional data streams (eg, climate variables, genomic surveillance) and advanced analytics (eg, machine learning for risk prediction, network models for transmission dynamics) to further optimize outbreak preparedness and resource allocation. This framework underscores the transformative potential of integrated data systems in global measles elimination efforts.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamiaopen\/ooaf062","type":"journal-article","created":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T11:39:40Z","timestamp":1751024380000},"source":"Crossref","is-referenced-by-count":3,"title":["Measles Tracker: a near-real-time data hub for measles surveillance"],"prefix":"10.1093","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9485-3877","authenticated-orcid":false,"given":"Francesco","family":"Branda","sequence":"first","affiliation":[{"name":"Unit of Medical Statistics and Molecular Epidemiology, University Campus Bio-Medico of Rome , Rome, 00128 ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria","family":"Tomasso","sequence":"additional","affiliation":[{"name":"Translational Health Research Center, Texas State University , San Marcos, TX 78666,","place":["United States"]},{"name":"Department of Computer Science, Texas State University , San Marcos, TX 78666,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5991-4052","authenticated-orcid":false,"given":"Mohamed Mustaf","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Faculty of Medicine and Health Sciences, SIMAD University , Mogadishu 252, Somalia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Massimo","family":"Ciccozzi","sequence":"additional","affiliation":[{"name":"Unit of Medical Statistics and Molecular Epidemiology, University Campus Bio-Medico of Rome , Rome, 00128 ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabio","family":"Scarpa","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences, University of Sassari , Sassari, 07100 ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,6,27]]},"reference":[{"key":"2025062707393116600_ooaf062-B1","doi-asserted-by":"crossref","first-page":"e1361","DOI":"10.1016\/S2214-109X(20)30361-2","article-title":"Global health and data-driven policies for emergency responses to infectious disease outbreaks","volume":"8","author":"Kozlakidis","year":"2020","journal-title":"Lancet Glob Health"},{"key":"2025062707393116600_ooaf062-B2","doi-asserted-by":"crossref","first-page":"e005542","DOI":"10.1136\/bmjgh-2021-005542","article-title":"Challenges in reported COVID-19 data: best practices and recommendations for future epidemics","volume":"6","author":"Badker","year":"2021","journal-title":"BMJ Glob Health"},{"key":"2025062707393116600_ooaf062-B3","doi-asserted-by":"crossref","first-page":"2408","DOI":"10.1080\/02664763.2021.1928016","article-title":"Comparing and integrating US COVID-19 data from multiple sources with anomaly detection and repairing","volume":"50","author":"Wang","year":"2023","journal-title":"J Appl Stat"},{"key":"2025062707393116600_ooaf062-B4","first-page":"1700","article-title":"Progress toward regional measles elimination\u2014worldwide, 2000\u20132019","volume-title":"MMWR. 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