R_t Covid-19 (India)

About R_t

R_t represents the effective reproduction rate of the virus calculated for each state. It predicts how many secondary infections may occur from one infected person. Values over 1.0 mean we should expect more cases in that area, values under 1.0 mean we should expect fewer.

The metric we need to be managing COVID-19: An excellent article by Kevin Systrom that explains why this metric is important

Like others, we have been frustrated with how data is being presented in different ways to further a message. Example: There are people and news organizations that are projecting the growth numbers on a log scale to diminish the magnitude of the problem, or to prove a 'flattening' of the curve. With a log scale, large numbers naturally get flattened! This site was developed out a frustration with existing data sources and their representations.

Disclaimer

• We are sourcing our Covid-19 data for India from multiple sources. Often these sources are not reliable, and change on a daily basis. If you know of a deemed-reliable source of data, please do let us know.
• This site is built solely to further our understanding of the virus. The predictions of R_t are not perfect. Please verify the results with other sources prior to drawing conclusions.
• We are seeing some interesting behavior with R_t changing in many states in India, over time. While the hope is to make the line turn green, we have seen setbacks as well. The reasons for these setbacks are not easy to comprehend, let alone model. We welcome your inputs on this.

Credits

• Inspiration: Thanks to Kevin Systrom and Thomas Vladeck for the original concept and implementation of R_t, which you can access at rt.live
• Data analysis and site developed by Ganesh, Jebin, and Marvel. Contact us at fructif.ai@gmail.com
  
• Data analysis using Python, visualizations built using plotly.com