Every
year, the scientists responsible for selecting strains for the influenza
vaccine must make their choice approximately six months before the flu season
begins. They are predicting the future: which variants of a rapidly mutating
virus will be circulating in the northern hemisphere by winter, in sufficient
quantities and with sufficient immune evasion to make strain selection
matter. The World Health Organization convenes expert committees, analyses
global surveillance data, and makes the best call available. Occasionally
that call is wrong, and in years when it is, vaccine effectiveness drops
sharply. The 2014-2015 season saw effectiveness fall to around 19 per cent,
with significant consequences for hospitalisation rates and mortality in
vulnerable groups.
Researchers at MIT have developed a system that may substantially
improve the odds. VaxSeer, unveiled in August 2025 and reported by MIT
News, is an AI-driven tool that analyses viral genetic sequences
and laboratory data to forecast which influenza strains are most likely to
dominate in a given season. Unlike traditional approaches that rely primarily
on expert consensus applied to surveillance data, VaxSeer combines
evolutionary modelling with antigenicity predictions in a dual framework that
can account for both how viruses are likely to evolve and how well candidate
vaccine strains are likely to match the circulating population when the
season arrives.
How VaxSeer Works
The system’s architecture integrates two analytical streams. The
first models viral evolution using sequence data: how influenza strains have
mutated historically and how those mutation patterns predict which variants
are likely to become dominant. The second stream assesses antigenicity: the
degree to which the immune response generated by a candidate vaccine strain
would be expected to protect against the circulating strains that the
evolutionary model predicts. The combination allows VaxSeer to generate what
the researchers call coverage scores, quantitative estimates of how well each
candidate vaccine strain would perform against each plausible circulating
population.
The peer-reviewed results, published in Nature
Medicine, are striking. Retrospective testing showed that VaxSeer
would have recommended vaccine strains that outperformed the WHO’s actual
strain selections across several recent flu seasons. The methodology is
testable in hindsight because the actual circulating strains and the actual
vaccine effectiveness data from each season are known after the fact,
allowing researchers to score different selection strategies against observed
outcomes. VaxSeer’s retrospective recommendations consistently achieved
higher simulated effectiveness than the decisions that were made in real
time.
What This Means for Public Health
The implications for vaccine programmes extend beyond the
technical improvement in strain selection. Influenza vaccination is one of
the largest and most logistically complex public health operations conducted
annually worldwide. Hundreds of millions of doses are manufactured and
distributed based on strain selection decisions made months in advance. A
tool that can improve the accuracy of those decisions does not just raise
average vaccine effectiveness: it reduces the variance of outcomes, lowering
the frequency of bad seasons in which a mismatch between vaccine and circulating
strain leaves large portions of vaccinated populations with diminished
protection.
For health systems already under pressure from the cumulative
effects of multiple respiratory illness seasons, that reduction in variance
has direct resource implications. Hospital admissions and intensive care
occupancy during severe flu seasons are driven substantially by vaccine
mismatches. Better predictions translate, through a chain of clinical and
epidemiological effects, into measurable reductions in avoidable
hospitalisation and mortality. As we explored in our investigation of AI
systems in high-stakes decision-making contexts, the value of AI in
these settings is measured not in benchmark scores but in real-world outcomes
for real people whose health depends on getting the prediction
right.
The Regulatory and Adoption Challenge
Translating VaxSeer from a research tool into operational
infrastructure for WHO strain selection processes is a different and
considerably more complicated undertaking than demonstrating its
retrospective performance. The existing vaccine strain selection framework
involves an internationally coordinated network of laboratories, surveillance
systems, and expert committees operating across multiple WHO regional
offices. Integrating a new AI tool into this infrastructure requires
validation at scale, regulatory acceptance in multiple jurisdictions, and the
development of governance frameworks that determine how VaxSeer’s
recommendations are weighted against expert judgment when the two
diverge.
These are not insurmountable challenges, but they are real ones
that distinguish research publication from public health impact. The pattern
is familiar from other healthcare AI applications: tools that perform
impressively in controlled testing often face a long and difficult journey
from journal publication to clinical practice. The explainability question is
particularly relevant here. As our analysis of the
growing demand for transparent AI in consequential decisions found,
decision-makers in regulated domains need to be able to interrogate how a
system reached its recommendation, not just accept that it performs well on
average. VaxSeer will need to demonstrate not only that its recommendations
are better but that the reasoning behind them is interpretable to the
virologists and epidemiologists who must ultimately stand behind the
decisions made.
Precision Public Health at Scale
VaxSeer is part of a broader trend toward AI-assisted precision in
public health that extends well beyond influenza. Genomic surveillance of
pathogens, AI-driven epidemiological modelling, and machine learning
approaches to drug resistance prediction are all advancing simultaneously.
The common thread is that these tools are most valuable not as replacements
for human expertise but as instruments that process the scale and complexity
of biological data in ways that human analysis cannot efficiently
perform.
As we observed in our coverage of the
real-world costs embedded in AI systems, the relationship between
AI capability and real-world benefit is always mediated by the institutional
and regulatory contexts in which tools are deployed. VaxSeer demonstrates
that this relationship can run in the beneficial direction: that AI applied
with scientific rigour to a problem that genuinely benefits from algorithmic
pattern recognition can produce outcomes that expert consensus alone cannot
match. The challenge now is to build the regulatory and institutional
infrastructure that allows tools of this kind to reach their potential
benefit, rather than remaining indefinitely in the space between research
promise and operational reality.
The scale of what better flu vaccine selection could mean is worth
making concrete. The CDC estimated that during the 2022-2023 flu season,
influenza vaccination in the United States prevented approximately 5.2
million illnesses, 2.5 million medical visits, and 65,000 hospitalisations.
Those numbers assume a season in which vaccine effectiveness was reasonable.
In a year like 2014-2015, when effectiveness fell to around 19 per cent due
to a vaccine mismatch, the corresponding prevented outcomes would have been a
fraction of that. A tool that consistently improves strain selection accuracy
by even a few percentage points in effectiveness has public health impact at
a scale that exceeds most medical interventions of similar cost. The
challenge is that flu seasons where the vaccine matches well are invisible as
success stories, while the years when it does not generate visible evidence
of a systemic problem. VaxSeer offers the possibility of reducing the
frequency of the visible failures, and that is the appropriate metric against
which to measure its value.
The WHO’s current strain selection process, which has served
public health for decades, was designed for a world without access to the
scale of computational analysis that VaxSeer deploys. Augmenting it rather
than replacing it is both the scientifically prudent and institutionally
realistic path to adoption.
About the Author
Stuart Kerr is the Technology Correspondent for LiveAIWire. He
writes about artificial intelligence, emerging technology, and the forces
reshaping work, business, and society.