Estimating $R_t$: We estimate the reproduction numbers Using EpiEstim Package in R. Details on the package and its documentation are available at: https://cran.r-project.org/web/packages/EpiEstim/index.html. The package is based on the reference : A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics by
Anne Cori,Neil Ferguson, Christophe Fraser, Simon Cauchemez

Caution: The number of Active cases should be considered in conjunction while judging the true significance of $R_t$ value.

$R_t$ States of India

[Weekly Data in CSV]

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For all the graphs on this page, if you click on the image then it will display an interactive graph, where as you hover your mouse pointer over the graph annotations with details will be displayed.

We plot $R_t$ only upto 10 days prior to the last data point. This is because only a fraction of infections occuring in that time frame will have been reported already. The uncertainity of the estimate of $R_t$ will increase during this time period and therefore the estimates are cut off at one mean delay (from the input delay_distribution) before the end of the time series. We refer the reader to the tutorial accompanying the package where this is explained in detail.

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Suggested BibTeX citation

  
 @misc{inc19,
 Contributors = {ISI-IISC-Team: Sree Akshaya, Siva Athreya, ,   Soham Bakshi, Srivatsa B., Nitya Gadhiwala, Disha Hegde,
 Jainam Khakra, Rahul Kanekar, Jagadish Midthala,  Abhiti Mishra, Sandipan Mishra, Srigyan Nandi, 
 Nihesh Rathod, A. Y. Sarath, Prashanth Shivakumar, Rajesh Sundaresan, Srinidi Veeraraghavan.},
 TITLE = {Covid-19 States of India and Karnataka District Timeline 20-21},
 YEAR = {2020},
 NOTE = {at \url{http://www.isibang.ac.in/~athreya/incovid19}}}