解码数据科学升级:疾病控制和预防中心5年数据科学项目的见解,2019-2023年
Mayer Antoine1, Adebowale I Ojo, Mary Catherine Bertulfo
1Author Affiliations: Division of Workforce Development, National Center for State, Tribal, Local, and Territorial Public Health Infrastructure and Workforce (NCSTLTPHIW), Centers for Disease Control and Prevention (CDC), Atlanta, Georgia (Mr Antoine, Drs Ojo, Bertulfo, Okomo-Adhiambo, and Kirkcaldy), and United States Public Health Service, Rockville, Maryland (Dr Kirkcaldy).
Journal of public health management and practice : JPHMP
|January 23, 2026
概括
公共卫生组织越来越多地使用数据科学,项目重点是可视化和AI/ML. 需要提高技能和现代化才能充分利用这些数据科学技术.
科学领域:
- 公共卫生 数据科学 数据科学
- 劳动力发展 劳动力发展
- 数据现代化数据现代化
背景情况:
- 公共卫生组织认识到数据科学的潜力.
- 关于公共卫生中的数据科学应用存在一个知识差距.
研究的目的:
- 在公共卫生环境中提供数据科学应用的全面概述.
- 描述CDC数据科学升级 (DSU) 计划 (2019-2023) 的项目特点.
- 在公共卫生数据科学中指导未来的劳动力发展和数据现代化努力.
主要方法:
- 手动审查DSU申请和项目介绍 (2019-2023年).
- 基于领域,任务,数据科学主题/方法,数据模式,工具和编程语言的112个项目的分析.
- 在五个队伍中跟踪数据科学方法采用的趋势.
主要成果:
- 项目涉及传染病 (13%),COVID-19 (13%),以及疫苗 (11%).
- 数据可视化 (54%) 和统计 (51%) 是常见的;人工智能 (AI) 和机器学习 (ML) 的使用率从33% (2019) 增加到56% (2023).
- 项目支持决策 (52%) 和流程改进 (22%),使用R (55%),Python (56%),RStudio (50%) 和Jupyter笔记本 (41%).
结论:
- 优先考虑数据可视化工具突出了基础设施和培训的需求.
- 越来越多的人工智能和机器学习的采用需要在这些先进的方法上提高员工的技能.
- 有效的数据科学整合需要战略性劳动力发展和数据现代化.
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