## At a glance

- For U.S. states, CFA and NCIRD estimate the time-varying reproductive number,
*Rt*—a measure that helps quickly assess whether COVID-19 infections are increasing or decreasing. This helps public health practitioners prepare and respond.

## COVID-19

### Data Update

*R*

_{t }is estimated using data on Emergency Department visits reported through the National Syndromic Surveillance Program (NSSP). Prior to May 10,2024,

*R*was estimated using data on COVID-19 and influenza hospital admissions. Preliminary internal comparisons showed that data on emergency department visits and hospital admissions produced similar

_{t}*R*estimates. Estimates of influenza

_{t}*R*have ended for the 2023-2024 season and will begin again in fall 2024.

_{t}As of September 10, 2024, we estimate that COVID-19 infections are growing or likely growing in 3 states, declining or likely declining in 31 states, and are stable or uncertain in 13 states.

We estimate the time-varying reproductive number, *R** _{t}*, a measure of transmission based on data from incident COVID-19 emergency department visits. Epidemic status was determined by estimating the probability that

*R*

*is greater than 1 (map below). Estimated*

_{t}*R*

*values above 1 indicate epidemic growth.*

_{t }The second figure below shows the estimated *R** _{t}* and uncertainty interval from July 17, 2024 through September 10, 2024 for the U.S. and for each reported state. (Click on the map to view the data for a specific state). While

*R*

*tells us if the number of COVID-19 infections is likely growing or declining, it does not reflect the burden of COVID-19. View a summary of key data for COVID-19, influenza, and RSV.*

_{t}## Interpreting Rt

*R*is a data-driven measure of disease transmission._{t}*R*_{t}_{ }is an estimate on date*t*of the average number of new infections caused by each infectious person.*R*accounts for current population susceptibility, public health interventions, and behavior._{t}*R*> 1 indicates that infections are growing because, on average, each infected person is causing more than one new infection while_{t}*R*< 1 indicates that infections are declining._{t}*R*can be a leading indicator of increases or decreases in cases, hospitalizations, or deaths, because transmission occurs before case confirmation, hospitalization, or death._{t}- The uncertainty range for each
*R*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._{t} - When the data are sparse, the model used to generate
*R*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._{t }

### What *R*_{t}_{} can and cannot tell us

_{t}

**What R_{t}_{} can tell us: **

*R*can tell us whether a current epidemic is growing, declining, or remaining stable, and is an additional tool to help public health practitioners prepare and respond.

_{t}**What R_{t}_{} cannot tell us:**

*R*

_{t}_{}cannot tell us about the underlying

*burden*of disease, just the trend of transmission. An

*R*

_{t}_{}< 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

*R*

_{t}_{}.

## Caveats and limitations

*R*estimates are sensitive to assumptions about the generation interval distribution._{t}*R*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._{t}

## 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*>1, infections are growing_{t} - If 76%-90% of the credible interval distribution of
*R*> 1, infections are likely growing_{t} - If 26%-75% of the credible interval distribution of
*R*> 1, infections have an uncertain trend or are stable (in this case, the credible interval spans across 1, and contains a mix of values above and below 1.)_{t} - If 10%-25% of the credible interval distribution of
*R*> 1, infections are likely declining; this is equivalent to 75%-90% of the credible interval of_{t}*R*≤ 1_{t} - If <10% of the credible interval distribution of
*R*> 1, infections are declining; this is equivalent to >90% of the credible interval of_{t}*R*≤ 1_{t} - The data used to estimate
*R*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_{t}*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.

As of May 10, 2024, *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*, and the strategies CDC uses to validate the accuracy of estimates.

_{t}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.