Artificial intelligence for modelling infectious disease epidemics.

Journal: Nature
PMID: 39972226

Abstract

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI.

Authors

  • Moritz U G Kraemer
    Moritz U. G. Kraemer, DPhil, is in the Department of Zoology, University of Oxford, UK; Computational Epidemiology group, Boston Children's Hospital, Boston, MA; and Harvard Medical School, Harvard University, Boston, MA.
  • Joseph L-H Tsui
    Pandemic Sciences Institute, University of Oxford, Oxford, UK.
  • Serina Y Chang
    Department of Electrical Engineering and Computer Science, University of California Berkeley, Berkeley, CA, USA.
  • Spyros Lytras
    Division of Systems Virology, Department of Microbiology and Immunology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
  • Mark P Khurana
    Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
  • Samantha Vanderslott
    Pandemic Sciences Institute, University of Oxford, Oxford, UK.
  • Sumali Bajaj
    Department of Biology, University of Oxford, Oxford, UK.
  • Neil Scheidwasser
    Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
  • Jacob Liam Curran-Sebastian
    Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
  • Elizaveta Semenova
    Department of Epidemiology and Biostatistics, Imperial College London, London, UK.
  • Mengyan Zhang
    Machine Learning and Artificial Intelligence Future Science Platform, CSIRO, Canberra, ACT 2601, Australia.
  • H Juliette T Unwin
    School of Mathematics, University of Bristol, Bristol, UK.
  • Oliver J Watson
    MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK.
  • Cathal Mills
    Pandemic Sciences Institute, University of Oxford, Oxford, UK.
  • Abhishek Dasgupta
    Pandemic Sciences Institute, University of Oxford, Oxford, UK.
  • Luca Ferretti
    Pandemic Sciences Institute, University of Oxford, Oxford, UK.
  • Samuel V Scarpino
    Network Science Institute, Northeastern University, Boston, MA, United States.
  • Etien Koua
    World Health Organization Regional Office for Africa, Brazzaville, Congo.
  • Oliver Morgan
    WHO Hub for Pandemic and Epidemic Intelligence, Health Emergencies Programme, World Health Organization, Berlin, Germany.
  • Houriiyah Tegally
    Kwazulu-Natal Research and Innovation Sequencing Platform (KRISP), College of Health Sciences, K-RITH Tower Building, Nelson R Mandela School of Medicine, University of KwaZulu-Natal, 719 Umbilo Road, Durban, South Africa. houriiyah.tegally@gmail.com.
  • Ulrich Paquet
    DeepMind, London, United Kingdom.
  • Loukas Moutsianas
    Genomics England, London, UK.
  • Christophe Fraser
    Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
  • Neil M Ferguson
    MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK.
  • Eric J Topol
    Scripps Research Translational Institute, La Jolla, CA 92037, USA; Scripps Clinic Division of Cardiovascular Diseases, La Jolla, CA 92037, USA. Electronic address: etopol@scripps.edu.
  • David A Duchêne
    Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
  • Tanja Stadler
    Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.
  • Patricia Kingori
    The Ethox Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
  • Michael J Parker
    Pandemic Sciences Institute, University of Oxford, Oxford, UK.
  • Francesca Dominici
    Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Nigel Shadbolt
    Department of Computer Science, University of Oxford, Oxford, UK.
  • Marc A Suchard
    Department of Biomathematics, UCLA School of Medicine, CA, USA.
  • Oliver Ratmann
    Department of Mathematics, Imperial College London, London, UK.
  • Seth Flaxman
    Department of Mathematics and Data Science Institute, Imperial College London, London, SW7 2AZ, UK.
  • Edward C Holmes
    School of Medical Sciences, The University of Sydney, Sydney, New South Wales, Australia.
  • Manuel Gomez-Rodriguez
    Max Planck Institute for Software Systems, Kaiserslautern, Germany.
  • Bernhard Scholkopf
    Max Planck Institute for Intelligent Systems 72076 Tübingen Germany.
  • Christl A Donnelly
    Department of Infectious Disease Epidemiology, MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom.
  • Oliver G Pybus
    Department of Zoology, University of Oxford, Oxford, UK; Department of Pathobiology and Population Science, The Royal Veterinary College, London, UK.
  • Simon Cauchemez
    Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, U1332 INSERM, UMR2000 CNRS, Paris, France.
  • Samir Bhatt
    Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG, UK.

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