An AI-assisted forecasting model from Google finished first among individual-team submissions in the CDC’s evaluation of the 2025–26 U.S. flu season. Google_SAI-FluEns was assessed alongside other eligible approaches from academic, government and industry teams. The evaluation scored forecasts submitted during the season against hospital admissions observed later, giving the result a firmer footing than a demonstration built around historical data.
It is a concrete example of AI helping researchers build an algorithm for a public-health task. The CDC measured forecasting performance under a particular scoring method, though. It did not measure whether Google’s forecasts changed staffing decisions, reduced hospital strain or improved patient outcomes.
Google Led the Individual-Team Field
The CDC’s FluSight evaluation, published September 30, 2026, included 39 eligible models from a larger set of submissions. Google_SAI-FluEns led the individual-team entries on the report’s primary metric: average relative weighted interval score across the season and the jurisdictions included in scoring.
The CDC also evaluates its own FluSight ensemble, which combines forecasts from participating models and is used in the agency’s forecast messaging. That ensemble ranked seventh among the 39 models. Google’s result identifies the strongest individual-team submission under the CDC’s evaluation; the agency did not replace its ensemble with Google’s model.
Teams forecast weekly influenza hospital admissions for the current week and up to three weeks ahead. The evaluation covered state-level jurisdictions and Washington, D.C. National forecasts were excluded from the main scoring because of differences in scale, and Puerto Rico forecasts were excluded because of data availability. Models had to submit at least 75% of the relevant forecasts to qualify.
The field was substantial, though “39 models” does not mean 39 independent organizations. The CDC says 34 teams contributed forecasts from 53 unique models, of which 39 met the evaluation’s inclusion criteria. Google’s result is a comparison against many eligible forecasting approaches, not a claim that it beat 38 separate institutions.
The Score Rewards Useful Uncertainty, Not Just a Close Guess
A hospital planner needs more than a single predicted admission count. A plausible range helps account for demand that comes in higher or lower than the central estimate. The CDC’s primary metric, weighted interval score, evaluates those prediction intervals against the admissions eventually recorded. Lower scores are better.
The CDC reported relative weighted interval scores, comparing performance with a baseline that carries forward the previous week’s admission level while accounting for uncertainty. A relative score below one indicates performance better than that baseline. Thirty-three of the 39 evaluated models cleared it, according to the CDC. Google led the individual-team submissions in a field where many models were already better than that simple reference forecast.
The CDC’s scoring methods also limit how broadly the ranking should be read. The agency used log-transformed admission counts to reduce the influence of differences in count size between jurisdictions. It compared models on forecast targets they shared and aggregated performance across the season. A strong overall position does not establish that a model was best in every state, every week or every forecast horizon.
Point predictions tell only part of the story. A model can miss the eventual admission count yet provide a useful uncertainty range; it can also land near the count while expressing uncertainty poorly. The CDC separately examined coverage, or how often observed admissions fell within models’ prediction intervals. The ranking reflects the quality of probabilistic forecasts under the agency’s chosen evaluation, not simply which entry guessed the closest number most often.
The government result is more informative than a vendor-selected example. Its scope remains narrower than a claim that one algorithm is generally the best way to predict infectious disease.
Google Says AI Helped Develop the Forecasting Algorithm
Google attributes the forecasting approach to Empirical Research Assistance, or ERA, an AI-assisted research system it says generates optimization algorithms for scientific tasks. Its claim concerns how researchers developed the forecasting method. Google is not claiming that a general-purpose chatbot independently ran the CDC’s forecasting operation or made public-health decisions.
In an April account of its research, Google described a progression from retrospective work on COVID-19 hospitalization forecasts to weekly, real-time submissions for flu, COVID-19 and respiratory syncytial virus. The completed CDC flu evaluation puts the flu work to a more demanding test than a result demonstrated solely on past data. Submitted forecasts had to face admissions that were not yet known when the forecasts were made.
The evidence has two sources. The CDC independently scored the submitted forecasts and identified Google’s ranking. Google is the source for its account of ERA’s role in developing the approach. The CDC result supports a claim about forecasting performance; it does not, by itself, audit every step in Google’s AI-assisted research process or isolate how much of the final model’s performance came from ERA rather than other research decisions.
The AI system’s reported contribution was to help create a domain-specific forecasting algorithm whose output could then be tested in a recurring, externally evaluated task. Readers need not accept a broad claim about autonomous scientific discovery to recognize the narrower achievement: an approach Google says was developed with AI led a competitive government evaluation of flu forecasts.
Fast-Moving Flu Trends Remain a Hard Test
Season averages can conceal the weeks when a forecast matters most. Hospital systems have particular reason to care about the onset of a surge or a sharp change near the peak, when trends become difficult to extrapolate.
The CDC reported declines in forecast performance around rapidly changing influenza trends during the 2025–26 season. Its report shows that the FluSight ensemble’s prediction intervals failed to anticipate increases in admissions in late December 2025 and decreases in mid-January 2026. Those are documented difficulties for the CDC ensemble, not specific errors that can be attributed to Google_SAI-FluEns without corresponding evidence about Google’s forecasts for those weeks.
Frequently Asked Questions
4 questions
1Did Google’s AI flu model rank first in the CDC evaluation?
Google_SAI-FluEns was the top-performing individual-team submission in the CDC’s evaluation of eligible 2025–26 flu hospitalization forecasting models. The CDC assessed 39 models using a season-long measure of forecast performance across included state-level jurisdictions. Its own FluSight ensemble, which it uses for forecast messaging, ranked seventh overall.
2
Sources
- CDC’s FluSight evaluationcdc.gov
- Empirical Research Assistance, or ERAblog.google
- April account of its researchresearch.google
