Artificial intelligence for modelling infectious disease epidemics

• 成果类型: Review
• 署名作者: 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.; Duchene, 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; Schoelkopf, Bernhard; Donnelly, Christl A.; Pybus, Oliver G.; Cauchemez, Simon; Bhatt, Samir
• 署名单位: University of Oxford; University of Oxford; University of California System; University of California Berkeley; University of California System; University of California Berkeley; University of Tokyo; University of Copenhagen; University of Oxford; University of Oxford; Imperial College London; University of Oxford; University of Bristol; Imperial College London; University of Oxford; University of Oxford; Northeastern University; The Santa Fe Institute; World Health Organization; World Health Organization; Stellenbosch University; Scripps Research Institute; Swiss Federal Institutes of Technology Domain; ETH Zurich; Swiss Institute of Bioinformatics; University of Oxford; Harvard University; Harvard T.H. Chan School of Public Health; University of California System; University of California Los Angeles; Imperial College London; Imperial College London; University of Sydney; Max Planck Society; Max Planck Society; University of London; University of London Royal Veterinary College; Institut National de la Sante et de la Recherche Medicale (Inserm); Pasteur Network; Universite Paris Cite; Institut Pasteur Paris
• 刊物名称: Nature
• ISSN/ISSBN: 0028-2861
• DOI: 10.1038/s41586-024-08564-w
• 发表日期: 2025-02-20
• 页码: 623-635
• 关键词: influenza; language; networks; HEALTH; mcmc

摘要

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.