Public health surveillance is the foundation of effective public health practice. Public health surveillance is defined as the ongoing systematic collection, analysis, and interpretation of data, closely integrated with the dissemination of these data to the public health practitioners, clinicians, and policy makers responsible for preventing and controlling disease and injury.1 Ideally, surveillance systems should support timely, efficient, flexible, scalable, and interoperable data acquisition, analysis, and dissemination. However, many current systems rely on disease-specific approaches that inhibit efficiency and interoperability (eg, manual data entry and data recoding that place a substantial burden on data partners) and use slow, inefficient, out-of-date technologies that no longer meet user needs for data management, analysis, visualization, and dissemination.2-4 Advances in information technology, data science, analytic methods, and information sharing provide an opportunity to substantially enhance surveillance. As a global leader in public health surveillance, the Centers for Disease Control and Prevention (CDC) is working with public health partners to transform and modernize CDC's surveillance systems and approaches. Here, we describe recent enhancements in surveillance data analysis and visualization, information sharing, and dissemination at CDC and identify the challenges ahead.
