Arkansas
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Loading model data (7%)
Rt · Effective Reproduction Number
Rt is the average number of people who will become infected by a person infected at time t. If it’s above 1.0, COVID-19 cases will increase in the near future. If it’s below 1.0, COVID-19 cases will decrease in the near future.
Infections per capita
Infections per capita is our best estimate of how many individuals get infected every day - including infections which are never diagnosed through testing and never manifest in testing data. This estimate is presented as infections, per 100,000 individuals, per day.
Cumulative infections
Our best estimate of the number of infections that have occurred in this geography since the start of the pandemic on a per-capita basis. Displayed as infections per 100,000 individuals. Uncertainty pre-December 2021 is not represented.
This project is supported by Cooperative Agreement NU38OT000297 from the Centers for Disease Control and Prevention (CDC) and the Council of State and Territorial Epidemiologists (CSTE), SHEPheRD Contract 200-2016-91779 from the CDC, and the CDC Broad Agency Announcement Contract 75D30122C14697. This work does not necessarily represent the views of CDC or CSTE.
The effective reproductive number (Rt) is an important metric of epidemic growth. Rt is the average number of people that an individual infected on day t is expected to go on to infect. When Rt is above 1, we expect cases to increase in the near future. When Rt is below one, we expect cases to decrease in the near future.
Calculating Rt from the reported number of reported cases is complicated. People are typically diagnosed after they have already spread the disease, and many are not diagnosed at all. As diagnostic guidelines loosen and testing availability improves, we expect to see more cases, though the underlying incidence of disease may or may not have changed. Lags in diagnosis, diagnostic delays, and changing diagnostic guidelines will all impact case reports, and bias estimates of Rt.
We can avoid these biases by estimating Rt from the number of new infections that occur in a given week. We estimate new infections using a statistical model that combines information about reported cases, reported hospitalizations, reported administered first and booster doses of COVID-19 vaccines, the percentage of the population vaccinated, disease stage duration, and disease severity and mortality risks. Our infections metric takes into account the delays mentioned above, and includes individuals who haven't tested positive. Once we estimate the number of new infections each week, we can use that number to produce a more robust estimate of Rt. Present-day estimates of Rt are highly uncertain, and can change dramatically over time. We feel most confident about results for dates which are at least 2 weeks in the past. Additionally, Rt is easy to misinterpret. In many cases, we expect users will find our Infections per capita metric to be more useful. See here for a discussion of the pitfalls of Rt.
Contributors to this project include: Melanie H. Chitwood, Ted Cohen, Kenneth Gunasekera, Joshua Havumaki, Fayette Klaassen, Nicolas A. Menzies, Virginia E. Pitzer, Marcus Russi, Joshua Salomon, Nicole Swartwood, Joshua L. Warren, and Daniel M. Weinberger.
Compute and computational support provided by the Yale Center for Research Computing. We use Nextflow for orchestration.
Original site built by Mike Krieger, with thanks to Ryan O’Rourke and Thomas Dimson.
Visualizations built using d3 and react-vis; site built using Next.js.