Current Epidemic Trends (Based on Rt) for States | CFA: Modeling and Forecasting | CDC

We estimate the time-varying reproductive number, Rt, a measure of transmission based on data from incident emergency department (ED) visits. Epidemic status was determined by estimating the probability that Rt is greater than 1 (map below). Estimated Rt values above 1 indicate epidemic growth.

The second figure below shows the estimated Rt and uncertainty interval from December 25, 2024 through February 18, 2025 for the U.S. and for each reported state. (Click on the map to view the data for a specific state). While Rt tells us if the number of infections is likely growing or declining, it does not reflect the burden of disease. Rt should be used alongside other surveillance metrics (such as the percentage of ED visits, which are displayed in the callout boxes in the map) for a more complete picture. View a summary of key data for COVID-19, influenza, and RSV.

As of February 18, 2025, we estimate that COVID-19 infections are growing or likely growing in 1 state, declining or likely declining in 34 states, and not changing in 13 states.

As of February 18, 2025, we estimate that influenza infections are growing or likely growing in 6 states, declining or likely declining in 33 states, and not changing in 9 states.

What Rt can tell us: Rt can tell us whether a current epidemic trend is growing, declining, or not changing, and is an additional tool to help public health practitioners prepare and respond.

What Rt cannot tell us: Rt cannot tell us about the underlying burden of disease, just the trend of transmission. An Rt < 1 does not mean that transmission is low, just that infections are declining. It is useful to look at respiratory disease activity in conjunction with Rt.

Rt is defined as the average number of new infections caused by each infected person at a particular time, t. When Rt > 1, infections are growing, and when Rt < 1, infections are declining. The color categories in the maps above were determined by estimating a distribution of possible Rt values based on the observed emergency department visit data and model assumptions (formally, a “credible interval”). We then calculate the proportion of that credible interval where the Rt > 1. Credible intervals are determined using the EpiNow2 package, which uses a Bayesian model to estimate Rt, while adjusting for delays and reporting effects.

Rt estimates are derived from daily counts of new COVID-19 emergency department visits reported through the National Syndromic Surveillance Program. This Rt : Behind the Model article provides a more in-depth overview of the modeling approach used to estimate Rt, and the strategies CDC uses to validate the accuracy of estimates.

To estimate Rt, we fit Bayesian models to the data using the R packages EpiNow2, epinowcast, or using Stan models developed by the CDC Center for Forecasting and Outbreak Analytics. Following best practices, these models adjust for lags from infection to observation, incomplete observation of recent infection events, and day-of-week reporting effects, in addition to uncertainty from all these adjustments.