Precision global health: a roadmap for augmented action

Danny J. Sheath, Rafael Ruiz de Castañeda, Nefti Eboni Bempong, Mario Raviglione, Catherine Machalaba, Michael S. Pepper, Effy Vayena, Nicolas Ray, Didier Wernli, Gérard Escher, Francois Grey, Bernice S. Elger, Kaj Kolja Kleineberg, David Beran, J. Jaime Miranda, Mark D. Huffman, Fred Hersch, Fred Andayi, Samuel M. Thumbi, Valérie D’AcremontMary Anne Hartley, Jakob Zinsstag, James Larus, María Rodríguez Martínez, Philippe J. Guerin, Laura Merson, Vinh Kim Ngyuen, Frank Rühli, Antoine Geissbuhler, Marcel Salathé, Isabelle Bolon, Catharina Boehme, Seth Berkley, Alain Jacques Valleron, Olivia Keiser, Laurent Kaiser, Isabella Eckerle, Jürg Utzinger, Antoine Flahault

Research output: Contribution to journalReview articlepeer-review

4 Scopus citations


With increased complexity in various global health challenges comes a need for increased precision and the adoption of more tailored health interventions. Building on precision public health, we propose precision global health (PGH), an approach that leverages life sciences, social sciences, and data sciences, augmented with artificial intelligence (AI), in order to identify transnational problems and deliver targeted and impactful interventions through integrated and participatory approaches. With more than four billion Internet users across the globe and the accelerating power of AI, PGH taps on our current augmented capacity to collect, integrate, analyse and visualise large volumes of data, both non-specific and specific to health. With the support of governments and donors, and together with international and non-governmental organisations, universities and research institutions can generate innovative solutions to improve health and wellbeing of the most vulnerable populations around the world. In line with the Sustainable Development Goals, we propose here a road map for the development and implementation of PGH.

Original languageEnglish
Article number5
JournalJournal of Public Health and Emergency
StatePublished - Mar 2020


  • Digital health
  • artificial intelligence (AI)
  • big data
  • machine learning
  • one health
  • public health


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