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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’Acremont
  • Mary 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
  • University of Geneva
  • University of Milan
  • EcoHealth Alliance
  • University of Pretoria
  • ETH Zürich
  • University of Geneva
  • University of Hong Kong
  • Federal Polytechnic School of Lausanne
  • University Center for Legal Medicine
  • University of Basel
  • University of Geneva and Geneva University Hospitals
  • Universidad Peruana Cayetano Heredia
  • Northwestern University Feinberg School of Medicine
  • University of New South Wales
  • University of Sydney
  • Kenya Medical Research Institute
  • Washington State University Pullman
  • Vaud University Hospital Center
  • Swiss Tropical and Public Health Institute Swiss TPH
  • IBM Research-Zürich
  • Infectious Diseases Data Observatory
  • University of Oxford
  • Graduate Institute of International and Development Studies
  • University of Zurich
  • Foundation for Innovative New Diagnostics (FIND)
  • GAVI Alliance
  • CNRS-IRD-MNHN-Sorbonne Universités (UPMC)

Research output: Contribution to journalReview articlepeer-review

8 Scopus citations

Abstract

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
Volume4
DOIs
StatePublished - Mar 2020
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 4 - Quality Education
    SDG 4 Quality Education
  2. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure
  3. SDG 16 - Peace, Justice and Strong Institutions
    SDG 16 Peace, Justice and Strong Institutions

Keywords

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

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