AI Can Predict Disease Outbreaks and Revolutionize Pandemic Response

AI-driven models are reshaping how we predict and respond to infectious disease outbreaks, offering new tools to allocate resources efficiently, anticipate viral mutations, and accelerate vaccine development. Scientists emphasize collaboration, ethics, and transparency to maximize AI’s potential in safeguarding global health.

Perspective: Artificial intelligence for modelling infectious disease epidemics. Image Credit: Mila Supinskaya Glashchenko / Shutterstock

A study published in the journal Nature today outlines for the first time how advances in AI can accelerate breakthroughs in infectious disease research and outbreak response. 

The study, published following last week's AI Action Summit and amid increasing global debate on AI investment and regulation, emphasizes safety, accountability, and ethics in the deployment and use of AI in infectious disease research. 

The study, which calls for a collaborative and transparent environment—both in terms of datasets and AI models—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 clinical diagnostics, precision medicine, or supporting clinical treatment decisions. 

Instead, this review 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 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: 

• Promising advances in improving current models of disease spread, aiming to make modeling more robust, accurate, and realistic. 
• Progress has been made in pinpointing areas of high transmission potential, helping to ensure that limited healthcare resources are allocated efficiently. 
• Potential to improve genetic data in disease surveillance, ultimately accelerating vaccine development and the identification of new variants. 
• Potential to help determine the properties of new pathogens, predict their traits, and identify whether cross-species jumps are likely. 
• Predicting which new variants of already-circulating pathogens—such as SARS-CoV-2 and influenza viruses—might arise and which treatments and vaccines are best for reducing their impact. 
• Possible AI-aided integration of population-level data with data from individual-level sources – including wearable technologies such as heart rate and step counts – to better detect and monitor outbreaks. 
• AI can create a new interface between the highly technical science and healthcare professionals with limited training, improving capacity in settings that need these tools the most.

However, not all areas of pandemic preparedness and response will be equally impacted by advances in AI. For example, whereas protein language models hold great promise for speeding up an 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 they suggest that integrating 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 broader community, and the potential risks associated with deploying 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 for evaluating AI models and advocate for strong collaborations between government, society, industry, and academia to develop sustainable and practical models for improving human health.