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

Epidemic tendencies

We estimate the time-varying reproductive quantity, R_t, a measure of transmission based mostly on knowledge from incident emergency division (ED) visits. Epidemic standing was determined by estimating the likelihood that R_t is larger than 1 (map under). Estimated R_t values above 1 point out epidemic development.

The second determine under reveals the estimated R_t and uncertainty interval from Could 21, 2025 by July 15, 2025 for the U.S. and for every reported state. (Click on on the map to view the information for a particular state). Whereas R_t tells us if the variety of infections is probably going rising or declining, it doesn’t replicate the burden of illness. R_t needs to be used alongside different surveillance metrics (equivalent to the share of ED visits, that are displayed within the callout packing containers within the map) for a extra full image. View a summary of key data for COVID-19, influenza, and RSV.

COVID-19

As of July 15, 2025, we estimate that COVID-19 infections are rising or seemingly rising in 27 states, declining or seemingly declining in 1 state, and never altering in 17 states.

Decoding R_t

• R_t is a data-driven measure of illness transmission. R_t is an estimate on date t of the typical variety of new infections attributable to every infectious particular person. R_t accounts for present inhabitants susceptibility, public well being interventions, and conduct.
• R_t > 1 signifies that infections are rising as a result of, on common, every contaminated particular person is inflicting multiple new an infection whereas R_t < 1 signifies that infections are declining.
• R_t could be a leading indicator of will increase or decreases in instances, hospitalizations, or deaths, as a result of transmission happens earlier than case affirmation, hospitalization, or loss of life.
• The uncertainty vary for every R_t estimate determines the likelihood that infections are rising. For instance, if 75% of the uncertainty vary falls above 1, then there’s a 75% probability that the infections are rising in that location.
• When the information are sparse, the mannequin used to generate R_t estimates will are inclined to generate estimates nearer to 1 with broad credible intervals, which displays uncertainty within the true epidemic pattern throughout these time durations.

Caveats and limitations

• R_t estimates are delicate to assumptions in regards to the generation interval distribution.
• R_t estimates could also be over-or-underestimated if the proportion of infections that lead to emergency division visits modifications abruptly. These estimates could be impacted by shifts in medical severity, elevated or decreased use of medical testing, or modifications in reporting.

Strategies

R_{t is outlined as the typical variety of new infections attributable to every contaminated particular person at a specific time, t. When Rt > 1, infections are rising, and when Rt < 1, infections are declining. The colour classes within the maps above have been decided by estimating a distribution of attainable Rt values based mostly on the noticed emergency division go to knowledge and mannequin assumptions (formally, a “credible interval”). We then calculate the proportion of that credible interval the place the Rt > 1. Credible intervals are decided utilizing the EpiNow2 package deal, which makes use of a Bayesian mannequin to estimate Rt, whereas adjusting for delays and reporting results.}

• If >90% of the credible interval distribution of R_t >1, infections are rising
• If 76%-90% of the credible interval distribution of R_t > 1, infections are seemingly rising
• If 26%-75% of the credible interval distribution of R_t > 1, infections are usually not altering (on this case, the credible interval spans throughout 1, and comprises a mixture of values above and under 1.)
• If 10%-25% of the credible interval distribution of R_t > 1, infections are seemingly declining; that is equal to 75%-90% of the credible interval of R_t ≤ 1
• If <10% of the credible interval distribution of R_t > 1, infections are declining; that is equal to >90% of the credible interval of R_t ≤ 1
• The information used to estimate R_t are up to date often, and initially-reported counts would possibly later be revised. We manually overview the information weekly and sometimes exclude implausible outlier values, however should still estimate R_t.
• R_t was not estimated for states within the following instances: 1. fewer than 10 emergency division visits for the illness have been reported in every of the prior 2 weeks, 2. there have been detected anomalies in reported values, and three. the mannequin didn’t move checks for reliability.

R_t estimates are derived from each day counts of recent disease-specific emergency division visits reported by the National Syndromic Surveillance Program. This R_t : Behind the Model article supplies a extra in-depth overview of the modeling strategy used to estimate R_t, and the methods CDC makes use of to validate the accuracy of estimates.

To estimate R_{t, we match Bayesian fashions to the information utilizing the R packages EpiNow2, epinowcast, or utilizing Stan fashions developed by the CDC Heart for Forecasting and Outbreak Analytics. Following best practices, these fashions regulate for lags from an infection to commentary, incomplete commentary of latest an infection occasions, and day-of-week reporting results, along with uncertainty from all these changes.}

Glossary of phrases

• Technology interval: the interval between the an infection instances of an infector-infectee pair; i.e. the distinction within the time when a person (Individual j) is contaminated by an infector (Individual i) and the time when this infector (Individual i) was contaminated.
• Main indicator: a variable that gives an early indication of future tendencies in an outbreak, e.g., R_{t, as this metric estimates the variety of infections attributable to one contaminated particular person in close to real-time.}
• Lagging indicator: a variable that gives a lagged indication of future tendencies in an outbreak, e.g., COVID-19 deaths, as this end result occurs after instances have occurred.