From Data to Action: Population-Level Prevention and Control of Communicable Diseases

Communicable disease prevention and control depends on the timely generation, sharing, and application of data across surveillance networks, clinical settings, public health units, and communities. The COVID-19 pandemic exposed both the power and the limits of data-driven response: while digital surveillance, genomic sequencing, and real-time analytics accelerated detection, fragmented data ecosystems, interoperability gaps, and inequitable access to information slowed action. Similar challenges persist for influenza, measles, antimicrobial resistance, and emerging zoonotic threats. Translating raw data into effective public health decisions requires not only robust analytical capacity, but also data that is Findable, Accessible, Interoperable, and Reusable (FAIR) — across institutions, borders, and disciplines. There is a growing need to understand how data infrastructure, sharing practices, and analytic methods can be strengthened to support faster, more equitable, and more effective communicable disease responses.This Research Topic aims to advance understanding of how communicable disease prevention and control is operationalised across health system settings, with an explicit focus on the role of data — its collection, quality, governance, and translation into action. We invite contributions that showcase recent advances in surveillance systems, applied epidemiological analysis, outbreak detection, and intersectoral response, as well as those that critically examine how data flows between health sectors and how open, well-structured data accelerates public health impact.We especially welcome submissions that share the underlying datasets in a FAIR-compliant format. Frontiers’ FAIR² Data Management service enables authors to publish peer-reviewed, citable data articles alongside their research, with datasets made Findable, Accessible, Interoperable, Reusable — and AI-ready. This supports the reproducibility and reuse of disease surveillance and epidemiological data beyond the original study. By bringing together diverse perspectives, this Research Topic aims to highlight strategies that improve data-driven responsiveness, operational effectiveness, and equity in communicable disease control at the population level.Suitable themes for manuscripts include, but are not limited to:o Data systems for communicable disease surveillance — architectures, interoperability standards, real-time data pipelines, and cross-border data sharingo Practice-informed epidemiology and applied analysis — translating surveillance data into actionable epidemiological insightso Outbreak detection, investigation, and response — signal detection methods, genomic epidemiology, and operational case studieso Open and FAIR data in infectious disease research — data governance, sharing practices, and linked data articles using Frontiers FAIR² Data Managemento Infection prevention and control in practice — evidence-based implementation in healthcare and community settingso Intersectoral coordination in communicable disease prevention and control Topic editors: Ho Yeung Lam, YUNG WAI CHAN Source: https://www.frontiersin.org/research-topics/82711/from-data-to-action-population-level-prevention-and-control-of-communicable-diseases