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[](https://www.cdc.gov/) [CFA: Modeling and Forecasting](https://www.cdc.gov/cfa-modeling-and-forecasting/about/index.html) Explore This Topic [For Everyone](https://www.cdc.gov/cfa-modeling-and-forecasting/site.html#gen) - [Modeling and Forecasting](https://www.cdc.gov/cfa-modeling-and-forecasting/about/index.html) - [COVID-19 Ensemble Forecasts](https://www.cdc.gov/cfa-modeling-and-forecasting/covid19-data-vis/index.html) - [Measles Outbreak Simulator](https://www.cdc.gov/cfa-modeling-and-forecasting/measles-outbreak-simulator/index.html) - [Current Epidemic Trends (Based on *Rt*) for States](https://www.cdc.gov/cfa-modeling-and-forecasting/rt-estimates/index.html) - [Technical Briefs](https://www.cdc.gov/cfa-modeling-and-forecasting/technical-briefs/index.html) - [Modeling Handbook](https://www.cdc.gov/cfa-modeling-and-forecasting/modeling-handbook/index.html) - [View all](https://www.cdc.gov/cfa-modeling-and-forecasting/site.html#gen) **Related Topics:** [Center for Forecasting & Outbreak Analytics](https://www.cdc.gov/forecast-outbreak-analytics/index.html) \| [Our Work](https://www.cdc.gov/forecast-outbreak-analytics/our-work/index.html) \| [About Behind the Model](https://www.cdc.gov/cfa-behind-the-model/php/behind-the-model/index.html) \| [Qualitative Assessments](https://www.cdc.gov/cfa-qualitative-assessments/php/about/index.html) \| [Insight Net](https://www.cdc.gov/insight-net/php/about/index.html) [Home](https://www.cdc.gov/cfa-modeling-and-forecasting/about/index.html) [](https://www.cdc.gov/) [*search*](https://www.cdc.gov/cfa-modeling-and-forecasting/rt-estimates/index.html) [CFA: Modeling and Forecasting](https://www.cdc.gov/cfa-modeling-and-forecasting/about/index.html) Menu For Everyone - [Modeling and Forecasting](https://www.cdc.gov/cfa-modeling-and-forecasting/about/index.html) - [COVID-19 Ensemble Forecasts](https://www.cdc.gov/cfa-modeling-and-forecasting/covid19-data-vis/index.html) - [Measles Outbreak Simulator](https://www.cdc.gov/cfa-modeling-and-forecasting/measles-outbreak-simulator/index.html) - [Current Epidemic Trends (Based on \<em\>R\<sub\>t\</sub\>\</em\>) for States](https://www.cdc.gov/cfa-modeling-and-forecasting/rt-estimates/index.html) - [Technical Briefs](https://www.cdc.gov/cfa-modeling-and-forecasting/technical-briefs/index.html) - [Modeling Handbook](https://www.cdc.gov/cfa-modeling-and-forecasting/modeling-handbook/index.html) - [View All](https://www.cdc.gov/cfa-modeling-and-forecasting/site.html#gen) Related Topics - [Center for Forecasting & Outbreak Analytics](https://www.cdc.gov/forecast-outbreak-analytics/index.html) - [Our Work](https://www.cdc.gov/forecast-outbreak-analytics/our-work/index.html) - [About Behind the Model](https://www.cdc.gov/cfa-behind-the-model/php/behind-the-model/index.html) - [Qualitative Assessments](https://www.cdc.gov/cfa-qualitative-assessments/php/about/index.html) - [Insight Net](https://www.cdc.gov/insight-net/php/about/index.html) [CFA: Modeling and Forecasting](https://www.cdc.gov/cfa-modeling-and-forecasting/about/index.html) [Modeling and Forecasting](https://www.cdc.gov/cfa-modeling-and-forecasting/about/index.html) [COVID-19 Ensemble Forecasts](https://www.cdc.gov/cfa-modeling-and-forecasting/covid19-data-vis/index.html) [Measles Outbreak Simulator](https://www.cdc.gov/cfa-modeling-and-forecasting/measles-outbreak-simulator/index.html) [Current Epidemic Trends (Based on *Rt*) for States](https://www.cdc.gov/cfa-modeling-and-forecasting/rt-estimates/index.html) [Technical Briefs](https://www.cdc.gov/cfa-modeling-and-forecasting/technical-briefs/index.html) [Modeling Handbook](https://www.cdc.gov/cfa-modeling-and-forecasting/modeling-handbook/index.html) [View Menu](https://www.cdc.gov/cfa-modeling-and-forecasting/rt-estimates/index.html)  [Forecast Outbreak Analytics](https://www.cdc.gov/forecast-outbreak-analytics/index.html) # Current Epidemic Trends (Based on *Rt*) for States Apr. 3, 2026 For Everyone ## At a glance - For U.S. states, CFA and NCIRD estimate the time-varying reproductive number, *Rt*—a measure that helps quickly assess whether infections are increasing or decreasing. This helps public health practitioners prepare and respond.  ## Epidemic trends We estimate the time-varying reproductive number, *Rt*, a measure of transmission based on data from incident emergency department (ED) visits. The [method for determining epidemic status](https://www.cdc.gov/cfa-modeling-and-forecasting/rt-estimates/index.html#cdc_data_description_data_use_and_impact-methods) estimates the probability that *Rt* is greater than 1 (map below). Estimated *Rt* values above 1 indicate epidemic growth. The second figure below shows the estimated *Rt* and uncertainty interval from January 21, 2026 through March 31, 2026 for the U.S. and for each reported state. (Click on the map to view the data for a specific state). While *Rt* tells us if the number of infections is likely growing or declining, it does not reflect the burden of disease. *R**t* should be used alongside other surveillance metrics (such as the percentage of ED visits, which are displayed in the callout boxes in the map) for a more complete picture. [View a summary of key data for COVID-19, influenza, and RSV.](https://www.cdc.gov/respiratory-viruses/data/index.html) ## Epidemic trend summary COVID-19 Influenza RSV ## COVID-19 As of March 31, 2026, we estimate that COVID-19 infections are growing or likely growing in 0 states, declining or likely declining in 32 states, and not changing in 11 states. Previous estimates can be found on [data.cdc.gov](https://data.cdc.gov/Public-Health-Surveillance/CDC-Epidemic-Trends-and-Rt/5dqz-y4ea/about_data). Skip Over Map Container ### Epidemic Trends - Growing - Likely Growing - Not Changing - Likely Declining - Declining - Not Estimated **United States** - The weekly percentage of ED visits diagnosed with COVID-19 is very low. The COVID-19 epidemic trend is likely declining. - Probability COVID-19 epidemic is growing: 16% (likely declining) - *Rt* Estimate: 0\.96 (0.91 - 1.05) - % of ED visits (COVID-19): 0\.3% (very low) *Click on a state to see more information.* Skip Data Table Data Table | LocationSort by Location in descending order | |---| [Download Data (CSV)]() Skipped data table. Feb 8 Feb 15 Feb 22 Mar 1 Mar 8 Mar 15 Mar 22 Mar 29 0\.92 0\.94 0\.96 0\.98 1\.00 1\.02 1\.04 1\.06 1\.08 Growing → Growing → ← Declining ← Declining #### Infection Date #### *Rt* (Time-Varying COVID-19 Reproductive Number) United States 95% Credible Interval [Download COVID-19 chart data (CSV)](blob:https://www.cdc.gov/cbfa3ed7-e025-4a63-a1c2-1dff7f905006) ## Influenza As of March 31, 2026, we estimate that Influenza infections are growing or likely growing in 1 state, declining or likely declining in 35 states, and not changing in 11 states. Previous estimates can be found on [data.cdc.gov](https://data.cdc.gov/Public-Health-Surveillance/CDC-Epidemic-Trends-and-Rt/5dqz-y4ea/about_data). Skip Over Map Container ### Epidemic Trends - Growing - Likely Growing - Not Changing - Likely Declining - Declining - Not Estimated Skip Data Table Data Table | LocationSort by Location in descending order | |---| [Download Data (CSV)]() Skipped data table. ## RSV As of March 31, 2026, we estimate that RSV infections are growing or likely growing in 2 state, declining or likely declining in 37 states, and not changing in 8 states. Previous estimates can be found on [data.cdc.gov](https://data.cdc.gov/Public-Health-Surveillance/CDC-Epidemic-Trends-and-Rt/5dqz-y4ea/about_data). Skip Over Map Container ### Epidemic Trends - Growing - Likely Growing - Not Changing - Likely Declining - Declining - Not Estimated Skip Data Table Data Table | LocationSort by Location in descending order | |---| [Download Data (CSV)]() Skipped data table. ## Interpreting *Rt* ### What *Rt* can and cannot tell us **What *Rt* can tell us:** *Rt* can tell us whether a current epidemic trend is growing, declining, or not changing, 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*. - *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 (see definition)](https://www.cdc.gov/cfa-modeling-and-forecasting/rt-estimates/index.html#cdc_data_description_data_management-glossary-of-terms) 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 *Rt* 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 - *Rt* estimates are sensitive to assumptions about the [generation interval (see definition)](https://www.cdc.gov/cfa-modeling-and-forecasting/rt-estimates/index.html#cdc_data_description_data_management-glossary-of-terms) 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 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 *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 in the following cases: 1. in each of the prior 2 weeks, fewer than 10 (for COVID-19 and Influenza) or 5 (for RSV) emergency department visits were reported 2. there were detected anomalies in reported values, and 3. the model did not pass checks for reliability. *Rt* estimates are derived from daily counts of new disease-specific emergency department visits reported through the [National Syndromic Surveillance Program](https://www.cdc.gov/nssp/index.html). This *Rt*: Behind the Model article provides a [more in-depth overview of the modeling approach](https://www.cdc.gov/cfa-behind-the-model/php/data-research/rt-estimates/index.html) 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](https://epiforecasts.io/EpiNow2/), [epinowcast](https://package.epinowcast.org/), or using Stan models developed by the CDC Center for Forecasting and Outbreak Analytics. Following [best practices](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409), 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. ## On This Page - [Epidemic trends](https://www.cdc.gov/cfa-modeling-and-forecasting/rt-estimates/index.html#cdc_data_description_who_is_ahead-epidemic-trends "Epidemic trends") - [Epidemic trend summary](https://www.cdc.gov/cfa-modeling-and-forecasting/rt-estimates/index.html#cdc_data_description_data_sources-epidemic-trend-summary "Epidemic trend summary") - [COVID-19](https://www.cdc.gov/cfa-modeling-and-forecasting/rt-estimates/index.html#cdc_data_description_parti-covid-19 "COVID-19") - [Influenza](https://www.cdc.gov/cfa-modeling-and-forecasting/rt-estimates/index.html#cdc_data_description_data_systems-influenza "Influenza") - [RSV](https://www.cdc.gov/cfa-modeling-and-forecasting/rt-estimates/index.html#cdc_data_description_tools_use-rsv "RSV") - [Interpreting *Rt*](https://www.cdc.gov/cfa-modeling-and-forecasting/rt-estimates/index.html#cdc_data_description_what_the_data_includes-interpreting-rt "Interpreting Rt") - [Caveats and limitations](https://www.cdc.gov/cfa-modeling-and-forecasting/rt-estimates/index.html#cdc_data_description_how_the_data_is_interpreted-caveats-and-limitations "Caveats and limitations") - [Methods](https://www.cdc.gov/cfa-modeling-and-forecasting/rt-estimates/index.html#cdc_data_description_data_use_and_impact-methods "Methods") - [Glossary of terms](https://www.cdc.gov/cfa-modeling-and-forecasting/rt-estimates/index.html#cdc_data_description_data_management-glossary-of-terms "Glossary of terms") ## Related Pages - [COVID-19 Ensemble Forecasts](https://www.cdc.gov/cfa-modeling-and-forecasting/covid19-data-vis/index.html) - [Measles Outbreak Simulator](https://www.cdc.gov/cfa-modeling-and-forecasting/measles-outbreak-simulator/index.html) - [Technical Briefs](https://www.cdc.gov/cfa-modeling-and-forecasting/technical-briefs/index.html) - [Modeling Handbook](https://www.cdc.gov/cfa-modeling-and-forecasting/modeling-handbook/index.html) - [Modeling and Forecasting](https://www.cdc.gov/cfa-modeling-and-forecasting/about/index.html) [View All CFA: Modeling and Forecasting](https://www.cdc.gov/cfa-modeling-and-forecasting/site.html#gen) [Technical Briefs](https://www.cdc.gov/cfa-modeling-and-forecasting/technical-briefs/index.html) Apr. 3, 2026 [Sources](https://www.cdc.gov/cfa-modeling-and-forecasting/rt-estimates/index.html#content-sources) [Print](https://www.cdc.gov/cfa-modeling-and-forecasting/rt-estimates/index.html#print) [Share](https://www.cdc.gov/cfa-modeling-and-forecasting/rt-estimates/index.html#share) [Facebook](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Fwww.cdc.gov%2Fcfa-modeling-and-forecasting%2Frt-estimates%2Findex.html "Share to Facebook") [LinkedIn](https://www.linkedin.com/shareArticle?url=https%3A%2F%2Fwww.cdc.gov%2Fcfa-modeling-and-forecasting%2Frt-estimates%2Findex.html&title=Current%20Epidemic%20Trends%20\(Based%20on%20Rt\)%20for%20States "Share to LinkedIn") [Twitter](http://twitter.com/share?url=https%3A%2F%2Fwww.cdc.gov%2Fcfa-modeling-and-forecasting%2Frt-estimates%2Findex.html&text=Current%20Epidemic%20Trends%20\(Based%20on%20Rt\)%20for%20States "Share to Twitter") [Syndicate](https://tools.cdc.gov/medialibrary/index.aspx#/sharecontent "Embed this Page") Content Source: [Center for Forecasting and Outbreak Analytics](https://www.cdc.gov/forecast-outbreak-analytics/index.html) ## Related Pages - 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- For U.S. states, CFA and NCIRD estimate the time-varying reproductive number, *Rt*—a measure that helps quickly assess whether infections are increasing or decreasing. This helps public health practitioners prepare and respond.  ## Epidemic trends We estimate the time-varying reproductive number, *Rt*, a measure of transmission based on data from incident emergency department (ED) visits. The [method for determining epidemic status](https://www.cdc.gov/cfa-modeling-and-forecasting/rt-estimates/index.html#cdc_data_description_data_use_and_impact-methods) estimates the probability that *Rt* is greater than 1 (map below). Estimated *Rt* values above 1 indicate epidemic growth. The second figure below shows the estimated *Rt* and uncertainty interval from January 21, 2026 through March 31, 2026 for the U.S. and for each reported state. (Click on the map to view the data for a specific state). While *Rt* tells us if the number of infections is likely growing or declining, it does not reflect the burden of disease. *R**t* should be used alongside other surveillance metrics (such as the percentage of ED visits, which are displayed in the callout boxes in the map) for a more complete picture. [View a summary of key data for COVID-19, influenza, and RSV.](https://www.cdc.gov/respiratory-viruses/data/index.html) As of March 31, 2026, we estimate that COVID-19 infections are growing or likely growing in 0 states, declining or likely declining in 32 states, and not changing in 11 states. Previous estimates can be found on [data.cdc.gov](https://data.cdc.gov/Public-Health-Surveillance/CDC-Epidemic-Trends-and-Rt/5dqz-y4ea/about_data). Skip Over Map Container **United States** - The weekly percentage of ED visits diagnosed with COVID-19 is very low. The COVID-19 epidemic trend is likely declining. - Probability COVID-19 epidemic is growing: 16% (likely declining) - *Rt* Estimate: 0\.96 (0.91 - 1.05) - % of ED visits (COVID-19): 0\.3% (very low) *Click on a state to see more information.* Skip Data Table Data Table [Download Data (CSV)]() Skipped data table. Feb 8 Feb 15 Feb 22 Mar 1 Mar 8 Mar 15 Mar 22 Mar 29 0\.92 0\.94 0\.96 0\.98 1\.00 1\.02 1\.04 1\.06 1\.08 Growing → Growing → ← Declining ← Declining #### Infection Date #### *Rt* (Time-Varying COVID-19 Reproductive Number) United States 95% Credible Interval [Download COVID-19 chart data (CSV)](blob:https://www.cdc.gov/cbfa3ed7-e025-4a63-a1c2-1dff7f905006) ## Interpreting *Rt* ### What *Rt* can and cannot tell us **What *Rt* can tell us:** *Rt* can tell us whether a current epidemic trend is growing, declining, or not changing, 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*. - *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 (see definition)](https://www.cdc.gov/cfa-modeling-and-forecasting/rt-estimates/index.html#cdc_data_description_data_management-glossary-of-terms) 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 *Rt* 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 - *Rt* estimates are sensitive to assumptions about the [generation interval (see definition)](https://www.cdc.gov/cfa-modeling-and-forecasting/rt-estimates/index.html#cdc_data_description_data_management-glossary-of-terms) 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 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 *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 in the following cases: 1. in each of the prior 2 weeks, fewer than 10 (for COVID-19 and Influenza) or 5 (for RSV) emergency department visits were reported 2. there were detected anomalies in reported values, and 3. the model did not pass checks for reliability. *Rt* estimates are derived from daily counts of new disease-specific emergency department visits reported through the [National Syndromic Surveillance Program](https://www.cdc.gov/nssp/index.html). This *Rt*: Behind the Model article provides a [more in-depth overview of the modeling approach](https://www.cdc.gov/cfa-behind-the-model/php/data-research/rt-estimates/index.html) 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](https://epiforecasts.io/EpiNow2/), [epinowcast](https://package.epinowcast.org/), or using Stan models developed by the CDC Center for Forecasting and Outbreak Analytics. Following [best practices](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409), 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. Apr. 3, 2026