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Artificial intelligence for modelling infectious disease epidemics

Nature volume 638, pages 623–635 (2025)Cite this article

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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.

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Fig. 1: AI approaches to tackling key epidemiological questions.
Fig. 2: Classification of data types to investigate infectious diseases.
Fig. 3: Iterative approach to public health decision making.

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Acknowledgements

M.U.G.K. acknowledges funding from The Rockefeller Foundation (PC-2022-POP-005), Google.org, the Oxford Martin School Programmes in Pandemic Genomics and Digital Pandemic Preparedness (also C.F. and L.F.), European Union’s Horizon Europe programme projects MOOD (874850) and E4Warning (101086640), the John Fell Fund, a Branco Weiss Fellowship, Wellcome Trust grants 303666/Z/23/Z, 226052/Z/22/Z and 228186/Z/23/Z (also H.T.), United Kingdom Research and Innovation (APP8583) and the Medical Research Foundation (MRF-RG-ICCH-2022-100069, also H.T.), UK International Development (301542-403), the Bill & Melinda Gates Foundation (INV-063472) and Novo Nordisk Foundation (NNF24OC0094346, also H.T.); E.C.H. from a National Health and Medical Research Council (NHMRC) Investigator award and by AIR@InnoHK administered by the Innovation and Technology Commission, Hong Kong Special Administrative Region, China; and S. Bhatt from the MRC Centre for Global Infectious Disease Analysis (reference MR/X020258/1), funded by the UK Medical Research Council (MRC). This UK funded award is carried out in the framework of the Global Health EDCTP3 Joint Undertaking. S. Bhatt is funded by the National Institute for Health and Care Research (NIHR) Health Protection Research Unit in Modelling and Health Economics, a partnership between UK Health Security Agency, Imperial College London and LSHTM (grant code NIHR200908). S. Bhatt acknowledges support from the Novo Nordisk Foundation through The Novo Nordisk Young Investigator Award (NNF20OC0059309). S. Bhatt acknowledges the Danish National Research Foundation (DNRF160) through the chair grant, which also supports N. Scheidwasser, M.P.K. and J.L.C.-S. S. Bhatt acknowledges support from The Eric and Wendy Schmidt Fund For Strategic Innovation through the Schmidt Polymath Award (G-22-63345). O.R. acknowledges funding support from the Bill & Melinda Gates Foundation (OPP117509, OPP1084362), EPSRC (EP/X038440/1), NIH (R01AI155080) and the Moderna Charitable Foundation; and E.J.T. from the US National Institutes of Health grant UM1TR004407. M.A.S. is supported in part through the US National Institutes of Health grants R01 AI153044 and R01 AI162611. E.S. acknowledges support in part by the AI2050 program at Schmidt Futures (grant G-22-64476). S. Bajaj is supported by the Clarendon Scholarship and St Edmund Hall College, University of Oxford and NERC DTP (grant number NE/S007474/1). T.S. acknowledges funding of ETH Zürich; F.D. from the NIH U24ES035309; C.F. from the Moh Family Foundation; M.G.-R. from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 945719); D.A.D. from the Novo Nordisk Foundation through The Novo Nordisk Data Science Emerging Investigator Award (NNF23OC0084647); and S.F. and M.Z. from the Engineering and Physical Sciences Research Council (EP/V002910/2). J.L.-H.T. is supported by a Yeotown Scholarship from New College, University of Oxford. M.J.P.’s research on infectious disease ethics is supported by the Moh Family Foundation and Wellcome (221719). B.S. acknowledge funding by the Machine Learning Cluster of Excellence, EXC number 2064/1. H.T. acknowledges support to CERI from grants from the South African Medical Research Council (SAMRC) with funds received from the National Department of Health, the Rockefeller Foundation (HTH 017), the Abbott Pandemic Defense Coalition (APDC), the National Institute of Health USA (U01 AI151698) for the United World Antivirus Research Network (UWARN), the INFORM Africa project through IHVN (U54 TW012041) and the eLwazi Open Data Science Platform and Coordinating Center (U2CEB032224), the SAMRC South African mRNA Vaccine Consortium (SAMVAC), the European Union supported by the Global Health EDCTP3 Joint Undertaking and its members, the European Union’s Horizon Europe Research and Innovation Programme (101046041), the Health Emergency Preparedness and Response Umbrella Program (HEPR Program), managed by the World Bank Group (TF0B8412), the GIZ commissioned by the Government of the Federal Republic of Germany, the UK’s Medical Research Foundation (MRF-RG-ICCH-2022-100069) and the Wellcome Trust for the Global.health project (228186/Z/23/Z). C.M. is supported by a studentship from the UK’s Engineering and Physical Sciences Research Council; and C.A.D. by the UK National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Emerging and Zoonotic Infections in partnership with Public Health England (grant number: HPRU200907). The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the European Commission, NIH, NIHR, UK Health Security Agency or the Department of Health and Social Care, or the other funders.

Author information

Author notes

  1. These authors contributed equally: Moritz U. G. Kraemer, Joseph L.-H. Tsui, Serina Y. Chang, Samir Bhatt

  2. These authors jointly supervised this work: Moritz U. G. Kraemer, Oliver G. Pybus, Simon Cauchemez, Samir Bhatt

Authors and Affiliations

  1. Pandemic Sciences Institute, University of Oxford, Oxford, UK

    Moritz U. G. Kraemer, Joseph L.-H. Tsui, Samantha Vanderslott, Cathal Mills, Abhishek Dasgupta, Luca Ferretti, Christophe Fraser, Michael J. Parker, Christl A. Donnelly & Oliver G. Pybus

  2. Department of Biology, University of Oxford, Oxford, UK

    Moritz U. G. Kraemer, Joseph L.-H. Tsui, Sumali Bajaj & Oliver G. Pybus

  3. Department of Electrical Engineering and Computer Science, University of California Berkeley, Berkeley, CA, USA

    Serina Y. Chang

  4. UCSF UC Berkeley Joint Program in Computational Precision Health, Berkeley, CA, USA

    Serina Y. Chang

  5. Division of Systems Virology, Department of Microbiology and Immunology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Spyros Lytras

  6. Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark

    Mark P. Khurana, Neil Scheidwasser, Jacob Liam Curran-Sebastian, David A. Duchêne & Samir Bhatt

  7. Oxford Vaccine Group, University of Oxford and NIHR Oxford Biomedical Research Centre, Oxford, UK

    Samantha Vanderslott

  8. Department of Epidemiology and Biostatistics, Imperial College London, London, UK

    Elizaveta Semenova

  9. Department of Computer Science, University of Oxford, Oxford, UK

    Mengyan Zhang, Nigel Shadbolt & Seth Flaxman

  10. School of Mathematics, University of Bristol, Bristol, UK

    H. Juliette T. Unwin

  11. MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK

    Oliver J. Watson, Neil M. Ferguson & Samir Bhatt

  12. Department of Statistics, University of Oxford, Oxford, UK

    Cathal Mills & Christl A. Donnelly

  13. Doctoral Training Centre, University of Oxford, Oxford, UK

    Abhishek Dasgupta

  14. Institute for Experiential AI, Northeastern University, Boston, MA, USA

    Samuel V. Scarpino

  15. Santa Fe Institute, Santa Fe, NM, USA

    Samuel V. Scarpino

  16. World Health Organization Regional Office for Africa, Brazzaville, Congo

    Etien Koua

  17. WHO Hub for Pandemic and Epidemic Intelligence, Health Emergencies Programme, World Health Organization, Berlin, Germany

    Oliver Morgan

  18. Centre for Epidemic Response and Innovation (CERI), School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa

    Houriiyah Tegally

  19. African Institute for Mathematical Sciences (AIMS) South Africa, Muizenberg, Cape Town, South Africa

    Ulrich Paquet

  20. Genomics England, London, UK

    Loukas Moutsianas

  21. Scripps Research, La Jolla, CA, USA

    Eric J. Topol

  22. Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland

    Tanja Stadler

  23. Swiss Institute of Bioinformatics, Lausanne, Switzerland

    Tanja Stadler

  24. The Ethox Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK

    Patricia Kingori & Michael J. Parker

  25. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Francesca Dominici

  26. The Open Data Institute, London, UK

    Nigel Shadbolt

  27. Department of Biostatistics, University of California, Los Angeles, Los Angeles, USA

    Marc A. Suchard

  28. Department of Mathematics, Imperial College London, London, UK

    Oliver Ratmann

  29. Imperial-X, Imperial College, London, UK

    Oliver Ratmann

  30. School of Medical Sciences, The University of Sydney, Sydney, New South Wales, Australia

    Edward C. Holmes

  31. Max Planck Institute for Software Systems, Kaiserslautern, Germany

    Manuel Gomez-Rodriguez

  32. Max Planck Institute for Intelligent Systems and ELLIS Institute Tübingen, Tübingen, Germany

    Bernhard Schölkopf

  33. Department of Pathobiology and Population Sciences, The Royal Veterinary College, London, UK

    Oliver G. Pybus

  34. Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, U1332 INSERM, UMR2000 CNRS, Paris, France

    Simon Cauchemez

  35. Pioneer Centre for Artificial Intelligence University of Copenhagen, Copenhagen, Denmark

    Samir Bhatt

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  41. Samir Bhatt
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Contributions

S. Bhatt and M.U.G.K. conceptualized the study with input from O.G.P. and S.C.; J.L.-H.T. made the figures with input from S. Bhatt and M.U.G.K.; M.U.G.K. and S. Bhatt wrote the original draft with input from O.G.P. and S.C. All of the authors contributed to sections and reviewed, edited and approved the manuscript. S. Bhatt and M.U.G.K. administered the project.

Corresponding authors

Correspondence to Moritz U. G. Kraemer or Samir Bhatt.

Ethics declarations

Competing interests

S. Bhatt is a paid member of the Academic Council of the Schmidt Science Fellows programme outside the scope of this work. This affiliation is unrelated to the submitted work, and the programme does not stand to benefit from this publication. M.A.S. receives grants from the US National Institutes of Health within the scope of this work, and grants and contracts from the US Food and Drug Administration, the US Department of Veterans Affairs, and Johnson and Johnson, all outside the scope of this work. C.F. is a member of two committees that advise the UK Department of Health on emerging epidemics, namely NERVTAG and SPI-M. The other authors declare no competing interests.

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Kraemer, M.U.G., Tsui, J.LH., Chang, S.Y. et al. Artificial intelligence for modelling infectious disease epidemics. Nature 638, 623–635 (2025). https://doi.org/10.1038/s41586-024-08564-w

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