Branda Francesco, Tomasso Maria, Ahmed Mohamed Mustaf, Ciccozzi Massimo, Scarpa Fabio
Unit of Medical Statistics and Molecular Epidemiology, University Campus Bio-Medico of Rome, Rome, 00128 , Italy.
Translational Health Research Center, Texas State University, San Marcos, TX 78666, United States.
JAMIA Open. 2025 Jun 27;8(3):ooaf062. doi: 10.1093/jamiaopen/ooaf062. eCollection 2025 Jun.
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.
To address the major limitations of current measles surveillance systems-including data fragmentation and lack of standardization-we 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.
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.
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.
由于疫苗接种率下降、国际旅行以及持续存在的免疫差距，麻疹继续对全球公共卫生构成严重威胁。早期疫情检测和应对仍然受到分散的监测系统的阻碍，这些系统往往缺乏互操作性，限制了数据的可获取性。
为解决当前麻疹监测系统的主要局限性，包括数据碎片化和缺乏标准化，我们开发了麻疹追踪器，这是一个集成的近实时数据中心，利用公开可用来源集中并统一美国的麻疹监测数据。该系统汇总来自多个层面的数据，包括：（1）公共卫生机构的官方报告，（2）流行病学监测公告，以及（3）疫情报告，主要通过新闻网站或新闻聚合器获取。平台架构实现了（1）关键流行病学变量（病例数、疫苗接种覆盖率、年龄分层发病率）的地理空间归一化，以及（2）动态可视化界面，以支持基于证据的应对协调。
麻疹追踪器通过近乎实时地整合不同数据流来增强态势感知，能够快速在地理空间上检测疫情聚集区、绘制疫苗接种差距图，并支持对弱势群体进行动态风险分层。它仅作为官方公共卫生系统的补充工具，提供教育和态势感知，而不干扰接触者追踪、疫苗接种或疫情控制活动。
作为一个集中式、可扩展的工具，麻疹追踪器利用数字流行病学原理推进了麻疹监测。未来的迭代将纳入更多数据流（如气候变量、基因组监测）和先进分析方法（如用于风险预测的机器学习、用于传播动态的网络模型），以进一步优化疫情防范和资源分配。这个框架强调了集成数据系统在全球消除麻疹努力中的变革潜力。
