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, Samir Bhatt

Nature 638, 623-635 (2025)

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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.