BibTex format
@article{Kraemer:2025:10.1038/s41586-024-08564-w,
author = {Kraemer, MUG and Tsui, JLH and Chang, SY and Lytras, S and Khurana, MP and Vanderslott, S and Bajaj, S and Scheidwasser, N and Curran-Sebastian, JL and Semenova, E and Zhang, M and Unwin, HJT and Watson, OJ and Mills, C and Dasgupta, A and Ferretti, L and Scarpino, SV and Koua, E and Morgan, O and Tegally, H and Paquet, U and Moutsianas, L and Fraser, C and Ferguson, NM and Topol, EJ and Duchêne, DA and Stadler, T and Kingori, P and Parker, MJ and Dominici, F and Shadbolt, N and Suchard, MA and Ratmann, O and Flaxman, S and Holmes, EC and Gomez-Rodriguez, M and Schölkopf, B and Donnelly, CA and Pybus, OG and Cauchemez, S and Bhatt, S},
doi = {10.1038/s41586-024-08564-w},
journal = {Nature},
pages = {623--635},
title = {Artificial intelligence for modelling infectious disease epidemics},
url = {http://dx.doi.org/10.1038/s41586-024-08564-w},
volume = {638},
year = {2025}
}
RIS format (EndNote, RefMan)
TY - JOUR
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.
AU - Kraemer,MUG
AU - Tsui,JLH
AU - Chang,SY
AU - Lytras,S
AU - Khurana,MP
AU - Vanderslott,S
AU - Bajaj,S
AU - Scheidwasser,N
AU - Curran-Sebastian,JL
AU - Semenova,E
AU - Zhang,M
AU - Unwin,HJT
AU - Watson,OJ
AU - Mills,C
AU - Dasgupta,A
AU - Ferretti,L
AU - Scarpino,SV
AU - Koua,E
AU - Morgan,O
AU - Tegally,H
AU - Paquet,U
AU - Moutsianas,L
AU - Fraser,C
AU - Ferguson,NM
AU - Topol,EJ
AU - Duchêne,DA
AU - Stadler,T
AU - Kingori,P
AU - Parker,MJ
AU - Dominici,F
AU - Shadbolt,N
AU - Suchard,MA
AU - Ratmann,O
AU - Flaxman,S
AU - Holmes,EC
AU - Gomez-Rodriguez,M
AU - Schölkopf,B
AU - Donnelly,CA
AU - Pybus,OG
AU - Cauchemez,S
AU - Bhatt,S
DO - 10.1038/s41586-024-08564-w
EP - 635
PY - 2025///
SN - 0028-0836
SP - 623
TI - Artificial intelligence for modelling infectious disease epidemics
T2 - Nature
UR - http://dx.doi.org/10.1038/s41586-024-08564-w
VL - 638
ER -