Artificial intelligence for modelling infectious disease epidemics

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

doi:10.1038/s41586-024-08564-w

Artificial intelligence for modelling infectious disease epidemics

Moritz Kraemer^*, Joseph Tsui, Serina Chang, Spyros Lytras, Mark Khurana, Samantha Vanderslott, Sumali Bajaj, Neil Scheidwasser, Jacob Curran-Sebastian, Elizaveta Semenova, Mengyan Zhang, H Juliette T Unwin, Oliver Watson, Cathal Mills, Abhishek Dasgupta, Luca Ferretti, Samuel Scarpino, Etien Koua, Oliver Morgan, Houriiyah TegallyUlrich Paquet, Loukas Moutsianas, Christophe Fraser, Neil Ferguson, Eric Topol, David Duchene, Tanja Stadler, Patricia Kingori, Michael Parker, Francesca Dominici, Nigel Shadbold, Marc Suchard, Oliver Ratmann, Seth Flaxman, Edward Holmes, Manuel Gomez-Rodriguez, Bernhard Scholkopf, Christl Donnelly, Oliver Pybus, Simon Cauchemez, Samir Bhatt

^*Corresponding author for this work

Research output: Contribution to journal › Article (Academic Journal) › peer-review

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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.

Original language	English
Article number	eadp7977
Pages (from-to)	623–635
Number of pages	13
Journal	Nature
Volume	638
Issue number	8051
Early online date	19 Feb 2025
DOIs	https://doi.org/10.1038/s41586-024-08564-w
Publication status	Published - 20 Feb 2025

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© Springer Nature Limited 2025.

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Kraemer, M., Tsui, J., Chang, S., Lytras, S., Khurana, M., Vanderslott, S., Bajaj, S., Scheidwasser, N., Curran-Sebastian, J., Semenova, E., Zhang, M., Unwin, H. J. T., Watson, O., Mills, C., Dasgupta, A., Ferretti, L., Scarpino, S., Koua, E., Morgan, O., ... Bhatt, S. (2025). Artificial intelligence for modelling infectious disease epidemics. Nature, 638(8051), 623–635. Article eadp7977. https://doi.org/10.1038/s41586-024-08564-w

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Kraemer, M, Tsui, J, Chang, S, Lytras, S, Khurana, M, Vanderslott, S, Bajaj, S, Scheidwasser, N, Curran-Sebastian, J, Semenova, E, Zhang, M, Unwin, HJT, Watson, O, Mills, C, Dasgupta, A, Ferretti, L, Scarpino, S, Koua, E, Morgan, O, Tegally, H, Paquet, U, Moutsianas, L, Fraser, C, Ferguson, N, Topol, E, Duchene, D, Stadler, T, Kingori, P, Parker, M, Dominici, F, Shadbold, N, Suchard, M, Ratmann, O, Flaxman, S, Holmes, E, Gomez-Rodriguez, M, Scholkopf, B, Donnelly, C, Pybus, O, Cauchemez, S & Bhatt, S 2025, 'Artificial intelligence for modelling infectious disease epidemics', Nature, vol. 638, no. 8051, eadp7977, pp. 623–635. https://doi.org/10.1038/s41586-024-08564-w

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T1 - Artificial intelligence for modelling infectious disease epidemics

AU - Kraemer, Moritz

AU - Tsui, Joseph

AU - Chang, Serina

AU - Lytras, Spyros

AU - Khurana, Mark

AU - Vanderslott, Samantha

AU - Bajaj, Sumali

AU - Scheidwasser, Neil

AU - Curran-Sebastian, Jacob

AU - Semenova, Elizaveta

AU - Zhang, Mengyan

AU - Unwin, H Juliette T

AU - Watson, Oliver

AU - Mills, Cathal

AU - Dasgupta, Abhishek

AU - Ferretti, Luca

AU - Scarpino, Samuel

AU - Koua, Etien

AU - Morgan, Oliver

AU - Tegally, Houriiyah

AU - Paquet, Ulrich

AU - Moutsianas, Loukas

AU - Fraser, Christophe

AU - Ferguson, Neil

AU - Topol, Eric

AU - Duchene, David

AU - Stadler, Tanja

AU - Kingori, Patricia

AU - Parker, Michael

AU - Dominici, Francesca

AU - Shadbold, Nigel

AU - Suchard, Marc

AU - Ratmann, Oliver

AU - Flaxman, Seth

AU - Holmes, Edward

AU - Gomez-Rodriguez, Manuel

AU - Scholkopf, Bernhard

AU - Donnelly, Christl

AU - Pybus, Oliver

AU - Cauchemez, Simon

AU - Bhatt, Samir

PY - 2025/2/20

Y1 - 2025/2/20

N2 - 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.

AB - 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.

U2 - 10.1038/s41586-024-08564-w

DO - 10.1038/s41586-024-08564-w

M3 - Article (Academic Journal)

C2 - 39972226

SN - 0028-0836

VL - 638

SP - 623

EP - 635

JO - Nature

JF - Nature

IS - 8051

M1 - eadp7977

ER -

Artificial intelligence for modelling infectious disease epidemics

Abstract

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