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Pathways to strengthening the epidemic intelligence workforce.

Barbara Tornimbene1, Zoila Beatriz Leiva Rioja2, Olaolu Aderinola3

  • 1World Health Organization Hub for Pandemic and Epidemic Intelligence, Berlin, Germany. tornimbeneb@who.int.

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|February 28, 2025
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Summary
This summary is machine-generated.

Building a skilled public health workforce is essential for effective epidemic intelligence. Investing in data science training and digital tools enhances surveillance capabilities and promotes health equity globally.

Keywords:
Collaborative governanceData science capacityEpidemic intelligencePublic health surveillanceWorkforce development

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Area of Science:

  • Public Health
  • Epidemiology
  • Data Science

Background:

  • The global public health surveillance landscape requires a workforce skilled in data science and analysis.
  • The World Health Organization (WHO) Pandemic and Epidemic Intelligence Innovation Forum addresses workforce readiness and technological advancements.

Purpose of the Study:

  • To summarize discussions on workforce development and technology in epidemic intelligence.
  • To highlight the need for multidisciplinary teams with advanced data skills.

Main Methods:

  • Summarized discussions from the WHO Pandemic and Epidemic Intelligence Innovation Forum.
  • Reviewed the role of digital tools in data collection, integration, and analysis.
  • Examined case studies from Nigeria, Korea, the UK, and Colombia.

Main Results:

  • Multidisciplinary teams with advanced data skills are necessary for effective surveillance.
  • Digital tools are crucial for real-time tracking and interpretation of diverse data.
  • Inclusive representation, skill development, and interdisciplinary training are vital for capacity building.
  • Case studies demonstrate the impact of digital tools and local expertise in closing surveillance gaps.

Conclusions:

  • Enhancing workforce capacity requires collaborative surveillance and interdisciplinary training.
  • Resilient and inclusive public health systems depend on sustained institutional support, partnerships, and investment in data literacy.