Current Epidemic Growth Status (Based on Rt) for States and Territories

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As of May 10, 2024, Rt is estimated using data on emergency department visits reported through the National Syndromic Surveillance Program (NSSP). Prior to May 10, 2024, Rt 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 Rt estimates. Estimates of influenza Rt have ended for the 2023-2024 season and will begin again in fall 2024.

COVID-19

As of July 09, 2024, we estimate that COVID-19 infections are growing or likely growing in 45 states and territories, declining or likely declining in 0 states and territories, and are stable or uncertain in 3 states and territories.

The second figure below shows the estimated time-varying reproductive number, Rt, and uncertainty interval from May 15, 2024 through July 09, 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, Rt, a measure of transmission, based on data from incident COVID-19 emergency department visits. Epidemic status was determined by estimating the probability that Rt is greater than 1. While Rt 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.

Interpreting Rt

  • Rt is a data-driven measure of disease transmission. Rt  is an estimate on date t of the average number of new infections caused by each infectious person. Rt accounts for current population susceptibility, public health interventions, and behavior.
  • Rt > 1 indicates that infections are growing because, on average, each infected person is causing more than one new infection while Rt < 1 indicates that infections are declining.
  • Rt 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 Rt 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 Restimates will tend to generate estimates nearer to 1 with wide credible intervals, which reflects uncertainty in the true epidemic trend during these time periods.
  • What Rt can tell us: Rt 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.
  • 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.

Caveats and limitations

  • Rt estimates are sensitive to assumptions about the generation interval distribution.
  • Rt 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

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.

  • If >90% of the credible interval distribution of Rt >1, infections are growing
  • If 76%-90% of the credible interval distribution of Rt > 1, infections are likely growing
  • If 26%-75% of the credible interval distribution of Rt > 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.)
  • If 10%-25% of the credible interval distribution of Rt > 1, infections are likely declining; this is equivalent to 75%-90% of the credible interval of Rt ≤ 1
  • If <10% of the credible interval distribution of Rt > 1, infections are declining; this is equivalent to >90% of the credible interval of Rt ≤ 1
  • The data used to estimate Rt 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 Rt.
  • Rt was not estimated for states and territories 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, Rt estimates are derived from daily counts of new COVID-19 emergency department visits reported through the National Syndromic Surveillance Program. This ‘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.

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., Rt, 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.