Artificial intelligence for modelling infectious disease epidemics

Kraemer, Moritz U. G.; Tsui, Joseph L.-H.; Chang, Serina Y.; Lytras, Spyros; Khurana, Mark P.; Vanderslott, Samantha; Bajaj, Sumali; Scheidwasser, Neil; Curran-Sebastian, Jacob Liam; Semenova, Elizaveta; Zhang, Mengyan; Unwin, H. Juliette T.; Watson, Oliver J.; Mills, Cathal; Dasgupta, Abhishek; Ferretti, Luca; Scarpino, Samuel V.; Koua, Etien; Morgan, Oliver; Tegally, Houriiyah; Paquet, Ulrich; Moutsianas, Loukas; Fraser, Christophe; Ferguson, Neil M.; Topol, Eric J.; Duchêne, David A.; Stadler, Tanja; Kingori, Patricia; Parker, Michael J.; Dominici, Francesca; Shadbolt, Nigel; Suchard, Marc A.; Ratmann, Oliver; Flaxman, Seth; Holmes, Edward C.; Gomez-Rodriguez, Manuel; Schölkopf, Bernhard; Donnelly, Christl A.; Pybus, Oliver G.; Cauchemez, Simon; Bhatt, Samir

Artificial intelligence for modelling infectious disease epidemics

Moritz U. G. Kraemer (), Joseph L.-H. Tsui, Serina Y. Chang, Spyros Lytras, Mark P. Khurana, Samantha Vanderslott, Sumali Bajaj, Neil Scheidwasser, Jacob Liam Curran-Sebastian, Elizaveta Semenova, Mengyan Zhang, H. Juliette T. Unwin, Oliver J. Watson, Cathal Mills, Abhishek Dasgupta, Luca Ferretti, Samuel V. Scarpino, Etien Koua, Oliver Morgan, Houriiyah Tegally, Ulrich Paquet, Loukas Moutsianas, Christophe Fraser, Neil M. Ferguson, Eric J. Topol, David A. Duchêne, Tanja Stadler, Patricia Kingori, Michael J. Parker, Francesca Dominici, Nigel Shadbolt, Marc A. Suchard, Oliver Ratmann, Seth Flaxman, Edward C. Holmes, Manuel Gomez-Rodriguez, Bernhard Schölkopf, Christl A. Donnelly, Oliver G. Pybus, Simon Cauchemez and Samir Bhatt ()
Additional contact information
Moritz U. G. Kraemer: University of Oxford
Joseph L.-H. Tsui: University of Oxford
Serina Y. Chang: University of California Berkeley
Spyros Lytras: The University of Tokyo
Mark P. Khurana: University of Copenhagen
Samantha Vanderslott: University of Oxford
Sumali Bajaj: University of Oxford
Neil Scheidwasser: University of Copenhagen
Jacob Liam Curran-Sebastian: University of Copenhagen
Elizaveta Semenova: Imperial College London
Mengyan Zhang: University of Oxford
H. Juliette T. Unwin: University of Bristol
Oliver J. Watson: Imperial College London
Cathal Mills: University of Oxford
Abhishek Dasgupta: University of Oxford
Luca Ferretti: University of Oxford
Samuel V. Scarpino: Northeastern University
Etien Koua: World Health Organization Regional Office for Africa
Oliver Morgan: World Health Organization
Houriiyah Tegally: Stellenbosch University
Ulrich Paquet: Muizenberg
Loukas Moutsianas: Genomics England
Christophe Fraser: University of Oxford
Neil M. Ferguson: Imperial College London
Eric J. Topol: Scripps Research
David A. Duchêne: University of Copenhagen
Tanja Stadler: ETH Zürich
Patricia Kingori: University of Oxford
Michael J. Parker: University of Oxford
Francesca Dominici: Harvard T.H. Chan School of Public Health
Nigel Shadbolt: University of Oxford
Marc A. Suchard: Los Angeles
Oliver Ratmann: Imperial College London
Seth Flaxman: University of Oxford
Edward C. Holmes: The University of Sydney
Manuel Gomez-Rodriguez: Max Planck Institute for Software Systems
Bernhard Schölkopf: Max Planck Institute for Intelligent Systems and ELLIS Institute Tübingen
Christl A. Donnelly: University of Oxford
Oliver G. Pybus: University of Oxford
Simon Cauchemez: Institut Pasteur, Université Paris Cité, U1332 INSERM, UMR2000 CNRS
Samir Bhatt: University of Copenhagen

Nature, 2025, vol. 638, issue 8051, 623-635

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41586-024-08564-w

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