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CDC FluSight forecasts estimate future weekly influenza hospital admissions for the United States and jurisdictions. They do not calculate an individual’s chance of being hospitalized. To read one responsibly, check the place and week, whether the chart shows a count or rate, the forecast’s central estimate and uncertainty interval, and how complete hospital reporting is.
What a flu hospital forecast measures
CDC FluSight publishes short-horizon forecasts of new weekly laboratory-confirmed influenza hospital admissions, alongside historical reported counts. Its estimates describe a population in a specified jurisdiction and week—not a particular person’s medical risk. A state rate can help compare places of different population sizes, but it is not an exact per-capita risk for every resident.
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For any point on a chart, read these details together:
- Jurisdiction: the United States or the selected state or other jurisdiction.
- Issue date: when the forecast was published.
- Target week: the week for which admissions are forecast.
- Unit: a count of admissions or a population-adjusted rate.
- Central estimate and interval: the forecast summary and its range of uncertainty.
- Reporting completeness: how much hospital reporting is available for the relevant week.
These distinctions matter: a rate is not a count, and a forecast for one week or jurisdiction cannot be read as a current estimate for another.
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How to read the median and prediction intervals
The median is a central summary of the forecast, not a promise that the observed number will match it. The colored bands are prediction intervals: they show bounds around forecast estimates, conveying uncertainty. CDC’s guidance puts it plainly: “The colored areas on the graph show prediction intervals, which indicate the bounds of uncertainty around the forecast estimates.” A wider interval signals a broader range of plausible outcomes; neither a narrow nor a wide band guarantees that the eventual observation will fall inside it.
An interval’s realized performance is assessed over many forecasts by checking how often it contains the eventual observation. That empirical coverage is different from a guarantee for one forecast. During sharp changes, actual admissions can move outside the predicted range.
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What CDC’s 2025–2026 evaluation says—and does not say
CDC evaluated 39 eligible models drawn from 53 unique model submissions by 34 teams. Its FluSight ensemble ranked seventh overall by average relative weighted interval score (relative WIS) for the season across jurisdictions, excluding national forecasts. Twelve models consistently outperformed the baseline in every jurisdiction, and 33 of the 39 included models performed better than that baseline. The best individual team submission was Google_SAI-FluEns.
Relative WIS measures how consistent forecast intervals are with observed values; lower is better, and a relative WIS below 1 means performance beat the baseline. CDC also assessed coverage: how often intervals contained observed admissions. These scores depend on the season, targets, forecast horizons, jurisdictions and eligible submissions. They describe this evaluation, not a guarantee about future performance or proof that every AI model beats statistical or mechanistic approaches.
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The evaluation also shows why aggregate scores need context. Around the late-December 2025 increase and mid-January 2026 decrease in hospitalizations, the ensemble’s 50% and 95% prediction intervals did not anticipate the observed changes. For the week ending December 27, 2025, fewer than 25% of two-week-horizon intervals across jurisdictions captured the observations. Coverage stabilized near 95% beginning in February 2026. Those coverage figures summarize many intervals; they do not promise that any one interval will contain its outcome.
What “AI” means in this forecast context
“AI forecast” is not one standardized method or a label that, by itself, establishes accuracy. For its evaluation, CDC classified model components as statistical, mechanistic, and/or artificial intelligence or machine learning based on method descriptions supplied by forecasting teams. Categories could overlap, and ensemble status was recorded separately.
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CDC says its ensemble combines submitted models into one forecast; specifically, the FluSight ensemble is the median forecast among models that teams self-designated for inclusion. It is therefore useful to distinguish an individual team’s model from the ensemble, and to compare them using the same horizon, jurisdiction, metric and baseline—not the word “AI” alone.
Geography, rates and incomplete reporting
State admission rates use Census Vintage 2024 population estimates as denominators. CDC cautions that these population figures may not match the populations served by hospitals in each catchment area. Rates are a rough population-size adjustment for comparison, not precise local hospital-catchment rates.
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CDC also flags jurisdictions where fewer than 80% of hospitals reported for the most recent week and warns that lower reporting may affect forecast validity. Before comparing a state with another, check the chart’s selected week, whether you are viewing counts or rates, and the reporting-completeness information.
A dated example, not a current-season estimate
The last listed forecast for the 2025–2026 season was issued June 11, 2026, using observations current as of May 27. It projected 320 to 2,000 new laboratory-confirmed influenza hospital admissions nationally for the week ending June 13, 2026, and characterized the trend as stable or uncertain. This is a historical example with a specific issue date, target week and geography—not a live estimate for the 2026–2027 season. CDC said reporting for that season would begin again in fall 2026.
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