J Public Health Manag Pract. 2026 Mar-Apr 01;32(2):260-267. doi: 10.1097/PHH.0000000000002284. Epub 2025 Nov 24.
ABSTRACT
CONTEXT: Public health organizations are increasingly recognizing the value and potential of data science. However, a gap remains in understanding how data science is being applied in public health.
OBJECTIVE: This article provides a comprehensive overview of data science applications in real-world public health settings. By describing the characteristics of projects supported by the Centers for Disease Control and Prevention’s Data Science Upskilling (DSU) program during 2019-2023, we seek to guide future efforts in public health data science workforce development and data modernization.
METHODS: We manually reviewed DSU applications and final presentations about the projects compiled during 2019-2023. We analyzed projects based on 7 characteristics, including public health domain and task, data science topic and method, data modality, tools, and programming languages used.
RESULTS: DSU supported 112 data science projects across 5 annual cohorts (2019-2023). Many projects addressed the COVID-19 pandemic (13%), infectious diseases (13%), and vaccines (11%). Approximately half the projects used data visualization (54%) and statistics (51%), with 42% employing artificial intelligence (AI) and machine learning (ML). Furthermore, 52% of projects were designed to support decision making, and 22% sought to improve processes and programs. Learners primarily used RStudio (50%), Jupyter Notebooks (41%), and Power BI (26%), along with Python (56%) and R (55%). AI and ML use increased from 33% of projects in 2019 to 56% in 2023, demonstrating an evolving focus on advanced methodologies.
CONCLUSIONS: Many teams prioritized data visualization, such as dashboards and visualization tools to support decision making, indicating opportunities for additional infrastructure and training in this area. We observed increasing use of AI and ML, suggesting a need for staff upskilling in these domains. Optimally leveraging data science technologies will require workforce development strategies and data modernization efforts to keep pace with the rapidly evolving field.
PMID:41576408 | DOI:10.1097/PHH.0000000000002284
