Predicting pandemics: How AI is reshaping the fight against viral evolution

What if advanced technology could foresee pandemics before they begin? With the growing threat of more frequent and severe outbreaks, artificial intelligence (AI) is emerging as a powerful tool in the battle to predict and prevent the next global health crisis.

A world primed for pandemics

The risk of pandemics is rising due to a combination of environmental changes, urbanization, climate change, increased global travel, and the mounting issue of antibiotic resistance. These factors create a perfect storm for the emergence and spread of new pathogens. However, advancements in epidemiology, molecular biology, and public health are helping to counterbalance this threat. Among these innovations, AI is proving to be a game-changer, particularly in its ability to decode the genetic language of viruses and anticipate their future mutations.

The challenge of tracking viral evolution

RNA viruses, such as SARS-CoV-2, are notorious for their rapid mutation rates. Unlike DNA, which has built-in error-correction mechanisms, RNA allows for frequent replication errors, giving rise to countless variants. This genetic flexibility enables viruses to adapt swiftly to new environmental pressures, such as immune responses or antiviral treatments.

To illustrate, RNA viruses can be likened to a bouncing ball that changes slightly with every bounce. This adaptability makes them harder to "catch," as they continuously reshape themselves to survive. The rapid evolution of these viruses poses a significant challenge for researchers trying to stay ahead of their mutations.

In response to this complexity, researchers at Stanford University, led by Brian Hie and his team, have turned to large language models (LLMs)—AI algorithms originally developed for text analysis. By adapting these tools to study viral genomes, scientists can analyze vast amounts of genomic data and uncover patterns in viral evolution. Just as LLMs decipher sentences in human languages, they can decode the "molecular language" of viruses, identifying mutations that provide evolutionary advantages and predicting which variants might dominate in the future.

AI as a shield against future pandemics

The introduction of AI-powered tools, such as AlphaFold from DeepMind and ESM-2 from Meta, has revolutionized this field. These systems leverage massive datasets—17 million genomic sequences for SARS-CoV-2 alone—to predict viral mutations and their implications. For example, researchers at the University of Tokyo have developed CoVFit, a predictive model that has already anticipated the emergence of specific variants.

At Harvard University, Debora Marks and her team created EVEscape, a system capable of generating 83 potential versions of the SARS-CoV-2 spike protein. This protein is a key target for vaccines and treatments, as it plays a crucial role in viral infection. By simulating possible mutations, EVEscape enables researchers to evaluate the effectiveness of vaccines against future variants, providing a valuable head start in vaccine development.

These AI systems have delivered tangible results. In March 2024, when the JN.1 variant was prevalent worldwide, CoVFit identified three amino acid changes that could enhance its transmissibility. These mutations were later observed in real-world variants that gained dominance, demonstrating the model's predictive accuracy.

The road ahead: Challenges and opportunities

Despite their promise, AI models face limitations. One significant hurdle is the phenomenon of evolutionary leaps—sudden, simultaneous mutations that dramatically alter a virus's characteristics. Such was the case with the Omicron variant, which emerged with over 50 mutations, conferring increased transmissibility and immune evasion. Predicting these abrupt shifts remains a formidable challenge.

Researchers like David Robertson from the University of Glasgow are working to understand the boundaries of viral evolution and the factors that drive these unexpected leaps. By refining AI models and collecting at least five years' worth of viral evolution data, scientists aim to improve their ability to predict and respond to these unpredictable events.

Moreover, teams like Shusuke Kawakubo's in Tokyo are expanding their focus to other pathogens, such as the influenza virus, to enhance seasonal vaccine strategies and prepare for future outbreaks.

A race against time

RNA viruses are relentless in their ability to evolve, making it increasingly difficult to track and control their spread. While artificial intelligence has already made significant strides in forecasting viral mutations and developing effective medical countermeasures, gaps remain in our ability to predict sudden and dramatic changes.

As researchers continue to push the boundaries of AI's capabilities, the hope is that these tools will not only help us stay one step ahead of emerging pathogens but also provide a robust defense against the invisible threats of tomorrow.