How to identify the next dangerous virus before it spreads among people is the central question in a new Comment in The Lancet Infectious Diseases. In it, researchers discuss how AI, combined with the One Health approach, can contribute to improved prediction and surveillance.
“Artificial intelligence cannot by itself prevent pandemics, but the technology can be a powerful supplement to the knowledge and methods we already use. The better we become at integrating data from humans, animals, and the environment, the better prepared we will be,” says Professor Frank Møller Aarestrup from the DTU National Food Institute in Denmark, one of the authors of the Comment in the renowned medical journal.
It was co-authored by Professor Marion Koopmans from the Erasmus Medical Centre in the Netherlands. She warns that once a disease starts spreading, it is very hard to bring under control.
“The interventions required are drastic – as we saw during COVID-19. That is why it is crucial to detect new pathogens before they gain a foothold,” says Marion Koopmans, noting that once established, new diseases can become persistent challenges, as COVID-19 has also shown.
The team of authors, which also includes experts from Eötvös Loránd University (ELTE) in Hungary, the University of Bologna in Italy, and the UK Animal and Plant Health Agency, speaks from their experience as collaborators over years, focusing on One Health approaches to emerging disease preparedness in the VEO consortium – a European research initiative developing data-driven tools to detect and track emerging infectious diseases.
Pandemics often originate in animals
The outbreaks of diseases such as SARS-CoV-2, avian influenza, and mpox demonstrate the difficulty of controlling new potential epidemics. Many pathogens originate in animals, but when and where they will spill over into humans is unpredictable. The authors of the Comment highlight how climate change, intensive animal production, and human encroachment into natural habitats increase the risk of so-called spillover events – situations in which pathogens cross from animals to humans and, in the worst case, develop into epidemics. Spillovers have been likened to sparks: most extinguish, but some ignite fires that spread uncontrollably. Being able to detect such spillovers as early as possible is a challenge that the team has been studying using big data approaches.
AI can reveal patterns in complex datasets
Artificial intelligence can help to analyse such datasets from diverse sources – such as climate, land use, animal production, transport, population movements, and socio-economics. When these datasets are combined, AI can reveal patterns that would otherwise be difficult to discern.
“AI can help us identify where in the world surveillance should be intensified geographically, but also in specific animal species, in wastewater, or in humans. In this way, we can prioritise efforts where the risks are greatest, so-called hotspots,” says Frank Møller Aarestrup.
Genetic signals as early warning
Once such hotspots are predicted, metagenomic sequencing can be added as a catch-all approach for detection of pathogens, both known and new ones. Metagenomic sequencing is the analysis of genetic material – in samples from wastewater, air, food, or the environment. It is increasingly used to provide insight into a vast diversity of known and unknown microorganisms. Many of the genetic fragments identified are not yet characterised.
“When we sequence a sample, we may find millions of genetic fragments. Most resemble something familiar and harmless, but we are left with thousands of unknowns. Here, AI can help detect patterns and point to what might be dangerous,” explains Frank Møller Aarestrup.
Once it is clear there is a potential pathogen, questions can arise about how dangerous it is. The potential for viruses from animals to infect humans, spread and cause disease in part is embedded in the genetic code. AI-based tools can be used to predict how mutations might alter viral properties.
“We see huge developments in this area. AI-based protein models can provide an indication of what a mutation does to the structure of viruses, and how that then can be translated to risk of spread, or risk of severe disease. While challenging now, we see great potential for the use of AI to speed up risk assessment,” says Marion Koopmans.
AI as a co-scientist – opportunities and limitations
The comment also describes early prototypes of so-called AI “co-scientists”, capable of conducting an entire research cycle – from hypothesis generation and literature review to data analysis and reporting.
“I envisage AI becoming a recognised competence at the table – on a par with different types of researchers. AI can deliver analyses or suggestions that we as scientists can evaluate. In that way, the technology becomes a supplement that can strengthen our decision-making processes,” says Frank Møller Aarestrup.
“That also implies that we need to learn what our future role is as teachers and supervisors. How do we make sure that the novel ways of working provide trustworthy output? Will we be able to recognise mistakes with advancements of AI models? We also need to go back to the classroom. Really exciting,” says Marion Koopmans.
The authors conclude that artificial intelligence offers intriguing possibilities for enhancing pandemic preparedness. Still, it must be seen as a complement – not a replacement – to the classical surveillance and research approaches already in use.
Read more
The comment “Artificial intelligence and One Health: potential for spillover prediction?” was published in The Lancet Infectious Diseases and authored by Marion Koopmans (Erasmus MC), Istvan Csabai (ELTE), Daniel Remondini (University of Bologna), Emma Snary (Animal and Plant Health Agency), and Frank Møller Aarestrup (DTU).
Read more about VEO.
Read more about One Health at WHO’s website.
Contact
Professor Frank Møller Aarestrup, DTU National Food Institute, Denmark; fmaa@food.dtu.dk
Professor Marion Koopmans, Erasmus Medical Centre, the Netherlands; m.koopmans@erasmusmc.nl
