AI Advances Promise to Transform Pandemic Preparedness and Response

Can AI predict the next pandemic? A new study says yes

By Dr. Priyom Bose, Ph.D.Reviewed by Benedette Cuffari, M.Sc.Feb 25 2025 Advances in AI-driven modeling improve outbreak predictions, but success hinges on data accessibility. Study: Artificial intelligence for modelling infectious disease epidemics. Image Credit: Shutterstock AI Generator / Shutterstock.com Artificial intelligence (AI) has significantly improved the predictability of pathogenic emergence and transmissibility. A recent Nature study emphasizes that the continued success of this technology depends on data transparency and reduced training costs. How is AI used in healthcare? Infectious disease epidemiology focuses on the emergence and transmission of infectious diseases among the population and strategies to prevent, control, and mitigate disease outbreaks. Numerous AI-based applications have been developed to support human health, including patient diagnosis, decision support for doctors, and individual-level disease risk prediction. Currently, AI has been used to a lesser extent in infectious disease epidemiology, which may be attributed to challenges in obtaining large-scale, standardized, and representative data essential for training and evaluating AI or machine learning (ML) models with variable parameters. Nevertheless, newer AI models are associated with greater competence, even when taught with a smaller amount of data to answer epidemiological questions. The potential of AI applications in infectious disease epidemiology In the early stages of any infectious outbreak, it is crucial to understand disease severity and the epidemic potential of the pathogen. Since the true sequence of events and location of the original infection are often uncertain, researchers frequently experience difficulties in estimating the incubation period and transmission intensity from observational data. Bayesian data augmentation has been invaluable for improving parameter inference. Moreover, integrating AI in the Bayesian data augmentation approach has significantly improved scalability and inference of the models. Conventional mechanistic and semi-mechanistic disease transmission models provide important insights into viral transmission and are used to develop counterfactual scenarios. However, these models are associated with considerable computational costs, which are partly due to the extensive complexities involved in numerical methods and inference in a high-dimensional parameter space. Recent advances in AI modelling offer the possibility to accelerate inferences by using variational inference, thereby enhancing model complexity and realism. AI-accelerated methods can potentially reduce model run times from weeks to hours, which can create more opportunities to understand potential associations between individual transmission heterogeneity and population-level outcomes. The graph neural network (GNN) is a promising AI system that can improve the understanding and accurate forecasting of infectious disease dynamics. Recently, GNN models have accurately predicted coronavirus disease 2019 (COVID-19) cases per region and influenza-like illness rates. AI models are also applied to genomic data to elucidate virus lineages, viral origin, pathogenicity, transmissibility, and the pathogen's potential to evade immune responses. These models have improved the accuracy of phylogenetic inference, thereby offering a precise characterization of the infection process. How does AI help policymakers make public health decisions? During an infectious disease epidemic, policy decisions are often made based on estimates of the number of current cases and forecasts of future cases. Importantly, epidemic surveillance data are almost always affected by biases in reporting, testing, and sampling. During the COVID-19 pandemic, researchers significantly accelerated the progress towards the development of more standardized and rigorous models that allow policymakers make appropriate public health decisions. Foundation models from large deep neural networks are a powerful approach to explore and elucidate time-series surveillance data. New ML and AI approaches have substantially reduced the time required to run epidemiological models to analyze complex scenarios and their statistical uncertainties. Large language models (LLMs) provide summaries of complex quantitative models that are personalized to a decision maker's preferences. The successful and appropriate use of AI tools depends on the careful analysis and resolution of key ethical challenges. For example, AI tools for pandemic preparedness and prevention largely depend on fair practices for the collection, storage, and sharing of data, as this ensures widespread accessibility of AI models. Limitations and recommendations Current AI models often fail to provide mechanistic insights into the transmission process, lack the power to predict beyond previously observed data and scenarios, and cannot communicate key epidemiological questions and concepts. In the future, an AI-infectious disease assistant could be developed by integrating single task models into more general foundation models. The potential benefits of AI in public health depend on the availability and accessibility of representative data. A firm ethical framework for storing and sharing data is essential for successful applications of AI in epidemiology. After the COVID-19 pandemic, significantly more data has become available to teach novel AI models. Nevertheless, routine surveillance data for infectious diseases remains inaccessible to the broader community, which prevents the development of an improved disease modeling system. The restrictive application of AI models has been attributed to high training costs. Robust data transparency and ethical sharing will essential for developing highly accurate new models at a reduced cost. Journal reference: Kraemer, M. U., Tsui, J. L. H., Chang, S. Y., et al. (2025) Artificial intelligence for modelling infectious disease epidemics. Nature 638(8051); 623-635. doi:10.1038/s41586-024-08564-w

Advances in AI can help prepare the world for the next pandemic

A perspective paper published in Nature outlines for the first time how advances in AI can accelerate breakthroughs in infectious disease research and outbreak response. The study puts particular emphasis on safety, accountability and ethics in the deployment and use of AI in infectious disease research. Calling for a collaborative and transparent environment -- both in terms of datasets and AI models -- the study is a partnership between scientists from the University of Oxford and colleagues from academia, industry and policy organizations across Africa, America, Asia, Australia and Europe. So far, medical applications of AI have predominantly focused on individual patient care, enhancing, for example, clinical diagnostics, precision medicine, or supporting clinical treatment decisions. This review instead considers the use of AI in population health. The study finds that recent advances in AI methodologies are performing increasingly well even with limited data -- a major bottleneck to date. Better performance on noisy and limited data is opening new areas for AI tools to improve health across both high-income and low-income countries. Lead author Professor Moritz Kraemer from the University of Oxford's Pandemic Sciences Institute, said, "In the next five years, AI has the potential to transform pandemic preparedness. "It will help us better anticipate where outbreaks will start and predict their trajectory, using terabytes of routinely collected climatic and socio-economic data. It might also help predict the impact of disease outbreaks on individual patients by studying the interactions between the immune system and emerging pathogens. "Taken together and if integrated into countries' pandemic response systems, these advances will have the potential to save lives and ensure the world is better prepared for future pandemic threats." Opportunities for AI and pandemic preparedness identified in the research include: Not all areas of pandemic preparedness and response will be equally impacted by advances in AI, however. For example, whereas protein language models hold great promise for speeding up understanding of how virus mutations can impact disease spread and severity, advances in foundational models might only provide modest improvements over existing approaches to modeling the speed at which a pathogen is spreading. The scientists urge caution in suggesting that AI alone will solve infectious disease challenges, but that integration of human feedback into AI modeling workflows might help overcome existing limitations. The authors are particularly concerned with the quality and representativeness of training data, the limited accessibility of AI models to the wider community, and potential risks associated with the deployment of black-box models for decision making. Study author Professor Eric Topol, MD, founder and director of the Scripps Research Translational Institute, said, "While AI has remarkable transformative potential for pandemic mitigation, it is dependent upon extensive worldwide collaboration and from comprehensive, continuous surveillance data inputs." Study lead author Samir Bhatt from the University of Copenhagen and Imperial College London, said, "Infectious disease outbreaks remain a constant threat, but AI offers policymakers a powerful new set of tools to guide informed decisions on when and how to intervene." The authors suggest rigorous benchmarks to evaluate AI models, advocating for strong collaborations between government, society, industry and academia for sustainable and practical development of models for improving human health.

Advances in AI can help prepare the world for the next pandemic, global group of scientists find

The study -- which is published following last week's AI Action Summit and amidst increasing global debate on AI investment and regulation -- puts particular emphasis on safety, accountability and ethics in the deployment and use of AI in infectious disease research. Calling for a collaborative and transparent environment -- both in terms of datasets and AI models -- the study is a partnership between scientists from the University of Oxford and colleagues from academia, industry and policy organisations across Africa, America, Asia, Australia and Europe. So far, medical applications of AI have predominantly focused on individual patient care, enhancing for example clinical diagnostics, precision medicine, or supporting clinical treatment decisions. This review instead considers the use of AI in population health. The study finds that recent advances in AI methodologies are performing increasingly well even with limited data -- a major bottleneck to date. Better performance on noisy and limited data is opening new areas for AI tools to improve health across both high-income and low-income countries. Lead author Professor Moritz Kraemer from the University of Oxford's Pandemic Sciences Institute, said: "In the next five years, AI has the potential to transform pandemic preparedness. "It will help us better anticipate where outbreaks will start and predict their trajectory, using terabytes of routinely collected climatic and socio-economic data. It might also help predict the impact of disease outbreaks on individual patients by studying the interactions between the immune system and emerging pathogens. "Taken together and if integrated into countries' pandemic response systems, these advances will have the potential to save lives and ensure the world is better prepared for future pandemic threats." Opportunities for AI and pandemic preparedness identified in the research include: Not all areas of pandemic preparedness and response will be equally impacted by advances in AI, however. For example, whereas protein language models hold great promise for speeding up understanding of how virus mutations can impact disease spread and severity, advances in foundational models might only provide modest improvements over existing approaches to modelling the speed at which a pathogen is spreading. The scientists urge caution in suggesting that AI alone will solve infectious disease challenges, but that integration of human feedback into AI modelling workflows might help overcome existing limitations. The authors are particularly concerned with the quality and representativeness of training data, the limited accessibility of AI models to the wider community, and potential risks associated with the deployment of black-box models for decision making. Study author Professor Eric Topol, MD, founder and director of the Scripps Research Translational Institute, said: "While AI has remarkable transformative potential for pandemic mitigation, it is dependent upon extensive worldwide collaboration and from comprehensive, continuous surveillance data inputs." Study lead author Samir Bhatt from the University of Copenhagen and Imperial College London said: "Infectious disease outbreaks remain a constant threat, but AI offers policymakers a powerful new set of tools to guide informed decisions on when and how to intervene." The authors suggest rigorous benchmarks to evaluate AI models, advocating for strong collaborations between government, society, industry and academia for sustainable and practical development of models for improving human health.