Current Epidemic Trends (Based on R_t) for States

Epidemic Trends

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

The second figure below shows the estimated R_t, and uncertainty interval from October 9, 2024 through December 3, 2024 for the United States and for each reported state. (Click on the map to view the data for a specific state). While R_t tells us if the number of infections is likely growing or declining, it does not reflect the burden of disease. View a summary of key data for COVID-19, influenza, and RSV.

COVID-19

As of December 3, 2024, we estimate that COVID-19 infections are growing or likely growing in 28 states, declining or likely declining in 3 states, and not changing in 17 states.

Influenza

As of December 3, 2024, we estimate that influenza infections are growing or likely growing in 40 states, declining or likely declining in 0 states, and not changing in 2 states.

Interpreting R_t

• R_t is a data-driven measure of disease transmission. R_t is an estimate on date t of the average number of new infections caused by each infectious person. R_t accounts for current population susceptibility, public health interventions, and behavior.
• R_t > 1 indicates that infections are growing because, on average, each infected person is causing more than one new infection while R_t < 1 indicates that infections are declining.
• R_t can be a leading indicator of increases or decreases in cases, hospitalizations, or deaths, because transmission occurs before case confirmation, hospitalization, or death.
• The uncertainty range for each R_t estimate determines the probability that infections are growing. For example, if 75% of the uncertainty range falls above 1, then there is a 75% chance that the infections are growing in that location.
• When the data are sparse, the model used to generate R_t estimates will tend to generate estimates nearer to 1 with wide credible intervals, which reflects uncertainty in the true epidemic trend during these time periods.

Caveats and limitations

• R_t estimates are sensitive to assumptions about the generation interval distribution.
• R_t estimates may be over-or-underestimated if the proportion of infections that result in emergency department visits changes abruptly. These estimates can be impacted by shifts in clinical severity, increased or decreased use of clinical testing, or changes in reporting.

Methods

R_t is defined as the average number of new infections caused by each infected person at a particular time, t. When R_t > 1, infections are growing, and when R_t < 1, infections are declining. The color categories in the maps above were determined by estimating a distribution of possible R_t 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 R_t > 1. Credible intervals are determined using the EpiNow2 package, which uses a Bayesian model to estimate R_t, while adjusting for delays and reporting effects.

• If >90% of the credible interval distribution of R_t >1, infections are growing
• If 76%-90% of the credible interval distribution of R_t > 1, infections are likely growing
• If 26%-75% of the credible interval distribution of R_t > 1, infections are not changing (in this case, the credible interval spans across 1, and contains a mix of values above and below 1.)
• If 10%-25% of the credible interval distribution of R_t > 1, infections are likely declining; this is equivalent 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; this is equivalent to >90% of the credible interval of R_t ≤ 1
• The data used to estimate R_t are updated frequently, and initially-reported counts might later be revised. We manually review the data weekly and occasionally exclude implausible outlier values, but may still estimate R_t.
• R_t was not estimated for states in the following cases: 1. fewer than 10 emergency department visits for COVID-19 were reported in each of the prior 2 weeks, 2. there were detected anomalies in reported values, and 3. the model did not pass checks for reliability.

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

To estimate R_t, 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.

Glossary of terms

• Generation interval: the interval between the infection times of an infector-infectee pair; i.e. the difference in the time when an individual (Person j) is infected by an infector (Person i) and the time when this infector (Person i) was infected.
• Leading indicator: a variable that provides an early indication of future trends in an outbreak, e.g., R_t, as this metric estimates the number of infections caused by one infected person in near real-time.
• Lagging indicator: a variable that provides a lagged indication of future trends in an outbreak, e.g., COVID-19 deaths, as this outcome happens after cases have occurred.