Pathways to strengthening the epidemic intelligence workforce.

Q2 Biochemistry, Genetics and Molecular Biology BMC Proceedings Pub Date : 2025-02-28 DOI:10.1186/s12919-025-00318-4
Barbara Tornimbene, Zoila Beatriz Leiva Rioja, Olaolu Aderinola, Zulma M Cucunubá, Catalina González-Uribe, Danil Mihailov, Steven Riley, Sang-Woo Tak, Oliver Morgan
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Abstract

The evolving landscape of public health surveillance demands a proficient and diverse workforce adept in data science and analysis. This report summarises discussions from the third session of the WHO Pandemic and Epidemic Intelligence Innovation Forum, focusing on workforce readiness and technological advancements in epidemic intelligence. The forum emphasizes the necessity of multidisciplinary surveillance teams equipped with advanced data skills. Digital tools play a transformative role in data collection and analysis, enabling real-time tracking, integration, and interpretation of diverse data sources. However, effective surveillance relies on inclusive representation and skill development. Collaborative surveillance and interdisciplinary training programs were emphasized as critical pathways to enhance workforce capacity, decision-making, and equity in public health. Case studies from Nigeria, Korea, the UK, and Colombia showcase the role of digital tools and contextual expertise in addressing surveillance gaps. Sustained institutional support, cross-sector partnerships, and investments in data literacy and workforce development are pivotal for creating resilient and inclusive public health systems.

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加强流行病情报工作队伍的途径。
不断发展的公共卫生监测领域需要熟练掌握数据科学和分析的熟练和多样化的劳动力。本报告总结了世卫组织大流行病和流行病情报创新论坛第三届会议的讨论,重点是流行病情报方面的劳动力准备和技术进步。论坛强调需要配备先进数据技能的多学科监测小组。数字工具在数据收集和分析中发挥着变革性作用,实现了对各种数据源的实时跟踪、集成和解释。然而,有效的监督依赖于包容性的代表和技能发展。强调协作监测和跨学科培训方案是提高公共卫生工作人员能力、决策和公平的关键途径。来自尼日利亚、韩国、英国和哥伦比亚的案例研究展示了数字工具和相关专业知识在解决监测差距方面的作用。持续的机构支持、跨部门伙伴关系以及对数据扫盲和劳动力发展的投资对于创建具有复原力和包容性的公共卫生系统至关重要。
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来源期刊
BMC Proceedings
BMC Proceedings Biochemistry, Genetics and Molecular Biology-Biochemistry, Genetics and Molecular Biology (all)
CiteScore
3.50
自引率
0.00%
发文量
6
审稿时长
10 weeks
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