New technical blog post from the CDC featuring epinowcast: Improving CDC’s Tools for Assessing Epidemic Growth

At CDC’s Center for Forecasting and Outbreak Analytics, we are building modeling tools and computational pipelines so that we can do complicated data analyses quickly and accurately in response to epidemics. Our goal is to make these tools accessible to federal, state, tribal, territorial, local, and academic partners. One of these efforts is to estimate the time-varying reproductive number, R_t, a measure that helps us quickly assess whether infections are increasing or decreasing.

Thought I would flag this nice new technical blog post from @kgostic and colleagues at the CDC highlighting the work the CFA is doing to improve the CDC’s tools for estimating epidemic growth.

For people in the US watch this space: “Coming Soon: See Jurisdiction-Specific Estimates of Epidemic Growth Status based on R_t, produced by CFA and NCIRD

There is also a nice video ( from @kgostic that covers some of the main challenges in real-time R_t estimation and plans for ongoing development (including epinowcast support).

This is an interesting form of blog given the wide audience it needs to hit so I’d be very interested to hear what people think. The only thing that really comes to mind to compare it to is the summary information put out by UKHSA for the COVID-19 R_t estimates used in the UK (The R value and growth rate - GOV.UK).


CDC Rt estimates for COVID-19 and Flu based on the methods laid out in this blog post are now online here (updated weekly): Current Epidemic Growth Status (Based on Rt) for States and Territories