Real-time effective reproduction rate (Rt) of the novel coronavirus, COVID-19. What is Rt?
Rt is the real-time effective reproduction rate of a virus, in this case COVID-19. The reproduction rate tells us the number of secondary infections to expect, on average, as a result of a single infected person spreading the virus. In other words, Rt tells us how quickly the virus is spreading at a specific point in time.
An Rt of one or greater means that the virus will spread rapidly until a majority of the population has been infected. An Rt of less than one means that the virus will spread more slowly and ultimately disappear before it's had the chance to infect everyone.
Understanding and tracking Rt at the regional and local levels are critical to managing outbreaks of COVID-19. Depending on how well we suppress this first wave, we can reasonably expect to see continued outbreaks across the country and world until a vaccine is developed and rolled out. Rt
The raw data came from the publicly available COVID-19 Data repository which is published and updated daily by The New York Times. The analysis of real-time effective reproduction comes from Kevin Systrom's work titled The Metric We Need to Manage COVID-19, which is itself based on an approach set forth in a peer-reviewed paper titled Real Time Bayesian Estimation of the Epidemic Potential of Emerging Infectious Diseases by Luís M. A. Bettencourt and Ruy M. Ribeiro.
Systrom's code to estimate the effective transmission rates of COVID-19 can be found on his GitHub profile, and the code used to generate these visualizations is available on my GitHub profile.
My name is Giles Van Gruisen. I'm a software developer and photographer based in New England, and I spend most of my time building data-driven software systems for work and fun. I enjoy exploring, analayzing, and visualizing interesting datasets to learn more about how the world works.
I am by no means an epidemiologist.
With this COVID-19 report, I'm doing my best to ensure the analysis is rooted in well-resesarched methods for modeling infectious diseases. I am trying to avoid speculative analysis or extrapolation beyond what is reasonable given the data available. If you have any questions, comments, or suggestions, please feel free to get in touch on Twitter or via email.