On September 30, the US Centers for Disease Control and Prevention released its review of 39 models that forecast weekly influenza hospital admissions during the 2025–26 season. Google’s SAI-FluEns ranked first among individual team submissions. The public forecast the CDC used was a separate median ensemble, which ranked seventh overall. At the national peak, weekly admissions exceeded 40,000; fewer than 25 percent of that ensemble’s two-week prediction intervals across jurisdictions contained the observed figures. Flu forecasts can help officials plan hospital beds, staffing and antiviral supplies, making the distinction between a contest winner and a public health forecast tangible.

The report shows accuracy declined when flu activity changed quickly. A season-wide score can hide how a model performs in a state or around a sudden surge, just when a hospital may need to revise staffing or bed plans. The review is a retrospective comparison; it does not say Google’s winning model has replaced the CDC’s ensemble or that hospitals used it to make those decisions. The next test is whether forecasts communicate uncertainty clearly and update local estimates early enough to inform response before admissions climb.