# Current Epidemic Growth Status (Based on *R*_{t}) for States and Territories

_{t}

## COVID-19

As of April 06, 2024, we estimate that COVID-19 infections are growing or likely growing in 2 states and territories, declining or likely declining in 26 states and territories, and are stable or uncertain in 21 states and territories.

The second figure below shows the estimated time-varying reproductive number, *R _{t}*, and uncertainty interval from February 15, 2024 through April 06, 2024 for the U.S. and for each reported state and territory. (Click on the map to view the data for a specific state or territory). Estimated values above 1 indicate epidemic growth.

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

*R*is greater than 1. While

_{t}*R*tells us if the number of COVID-19 infections are 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}## Influenza

As of April 06, 2024, we estimate that influenza infections are growing or likely growing in 3 states and territories, declining or likely declining in 32 states and territories, and are stable or uncertain in 11 states and territories.

The second figure below shows the estimated time-varying reproductive number, *R _{t}*, and uncertainty interval from February 15, 2024 through April 06, 2024 for the U.S. and for each reported state and territory. (Click on the map to view the data for a specific state or territory). Estimated values above 1 indicate epidemic growth.

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

*R*is greater than 1. While

_{t}*R*tells us if influenza infections are likely growing or declining, it does not reflect the burden of influenza. View a summary of key data for COVID-19, influenza, and RSV.

_{t}## Interpreting *R*_{t }

_{t }

*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*can tell us:_{t}*R*can tell us whether infections are growing, declining, or remaining stable, and is an additional tool to help public health practitioners prepare and respond._{t}**What***R*cannot tell us:_{t}*R*cannot tell us about the underlying_{t}*burden*of disease, just the trend of transmission. An*R*< 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_{t}*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 hospitalizations changes abruptly. These estimates can be impacted by shifts in clinical severity, increased or decreased use of clinical testing, or changes in reporting._{t }- While these estimates are based on a single data source (hospitalizations), studies have indicated that any resulting biases are likely minor and that this is a robust approach to estimate
*R*._{t}

*R _{t}* is defined as the average number of new infections caused by each infected person at a particular time, t. When

*R*> 1, infections are growing, and when

_{t}*R*< 1, infections are declining. The color categories in the maps above were determined by estimating a distribution of possible

_{t}*R*values based on the observed hospitalization data and model assumptions (formally, a “credible interval”). We then calculate the proportion of that credible interval where the

_{t}*R*> 1. Credible intervals are determined using the EpiNow2 package, which uses a Bayesian model to estimate

_{t}*R*, while adjusting for delays and reporting effects.

_{t}- 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} *R*_{t}was not estimated for states and territories in the following cases: 1. fewer than 10 laboratory-confirmed COVID-19 or influenza hospital admissions 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 or influenza hospitalizations. This blog post 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.

**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*, as this metric estimates the number of infections caused by one infected person in near real-time._{t}**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.