Reading Viral Blueprints: How AI Is Decoding the Next Pandemic Threat

Redacción HC
01/06/2023

As the world still grapples with the aftermath of COVID-19, scientists are urgently working on ways to anticipate — and prevent — the next outbreak. One of the most promising frontiers? Using genomic data and machine learning to identify viruses that could potentially infect humans before they ever do. A study published in PLOS Biology in 2021 presents a breakthrough: an AI-based predictive model that ranks the risk of animal viruses becoming zoonotic — using only their genome sequences.

This innovation could radically change how public health agencies prioritize virus surveillance and response — offering a glimpse into a future where science can outpace emerging pathogens.

The Problem: Predicting Viral Threats Before It’s Too Late

Roughly 75% of emerging infectious diseases in humans originate from animals. Yet the viral world remains vastly unexplored: scientists estimate that 1.67 million animal viruses remain uncharacterized. Traditional methods for assessing which of these might pose a danger to humans rely on phenotypic data — how the virus behaves — which usually becomes available after the pathogen has already emerged.

Given the surge in viral genome sequencing, researchers urgently need methods that can work with only genetic data — often the first (and only) clue available after a virus is discovered in nature.

This study, led by researchers from the University of Glasgow, aims to fill that gap using machine learning trained on genomic features alone — and the results are both powerful and timely.

How the Model Works: Reading the Language of Viral Genomes

The research team compiled a dataset of 861 viruses with known zoonotic status and trained gradient-boosted machine (GBM) models to detect “host range signatures” hidden in the viral genomes. These include:

• Codon usage bias
• Amino acid composition
• Dinucleotide frequencies
• Similarity to human gene transcripts, especially those related to immune response

These features act like molecular fingerprints, offering clues as to whether a virus has already adapted — or is predisposed — to infect human cells.

To test the model’s real-world applicability, they applied it to a separate set of 645 animal-associated viruses. The goal: prioritize which unknown viruses deserve urgent research attention based on their genetic make-up alone.

Key Findings: AI Outperforms Traditional Methods

The results were striking. The machine learning model outperformed phylogenetic similarity — the conventional approach that looks at how closely related a virus is to known human pathogens. The model achieved an AUC of 0.773, signaling strong predictive accuracy.

Among the 645 unknown viruses analyzed:

• 272 were flagged as high-risk
• 41 were categorized as very high-risk

Perhaps most impressively, the model successfully identified SARS-CoV-2 — the virus behind COVID-19 — as a high-risk coronavirus without any prior knowledge of related SARS-like viruses. This highlights the model’s prospective utility, not just retrospective analysis.

The research suggests that certain genomic traits are broadly predictive of a virus’s ability to jump species — independent of evolutionary lineage. These traits may effectively “preadapt” a virus for human infection, meaning that some threats may be hiding in plain genetic sight.

Why It Matters: From Outbreak Response to Outbreak Prevention

This technology could redefine viral surveillance and radically improve global preparedness for future pandemics.

1. Speed and Scale

Unlike traditional methods that require extensive lab testing, this genomic approach is fast, scalable, and low-cost. As more viral genomes are sequenced — often as part of biodiversity or animal health research — they can be immediately screened for zoonotic potential.

2. Resource Optimization

Health agencies can use the model to prioritize lab validation and field monitoring of the most concerning viruses. This is especially valuable in resource-limited settings where every sample counts.

3. Early-Stage Risk Assessment

The model offers a new layer of early warning, potentially identifying threats years before they cause human disease. As the authors note, genome-based triage can “direct limited experimental and surveillance resources to where they are most needed.”

A New Era for Pandemic Preparedness

This study marks a shift from reactive to proactive pandemic strategy. Instead of waiting for viruses to emerge, we now have tools to scan the viral universe for genetic red flags.

Future research will need to refine these models further — incorporating more genomic data, understanding the biological basis of these predictive signatures, and combining them with ecological context. But the foundation has been laid for an approach that blends AI, genomics, and public health into a formidable defense system.

"What we learn from the code of viruses today," the authors imply, "could save millions of lives tomorrow."

Conclusion: Turning Genomes into Forecasts

In a hyper-connected world, the next outbreak is not a question of if — but when. This pioneering study provides a blueprint for how we can use the growing sea of viral genome data to see ahead of the curve, identify emerging threats, and act before it’s too late.

From SARS-CoV-2 to future unknowns, the genetic signatures of danger are out there. Now, we have a way to read them.

Topics of interest

Health Technology