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COVID-19 Rt Analysis in Indian Context

Reproduction number and Growth rate comparison in India

Statewise Comparison of realtime Rt and Growth rate of Confirmed cases

What is Rt?

In any epidemic, $R_t$ is the measure known as the effective reproduction number. It’s the number of people who become infected per infectious person at time t. The most well-known version of this number is the basic reproduction number: $R_0$ when $t=0$. However, $R_0$ is a single measure that does not adapt with changes in behavior and restrictions.

How does it help?

As a pandemic evolves, increasing restrictions (or potential releasing of restrictions) changes $R_t$. Knowing the current $R_0$ is essential. When $R\gg1$, the pandemic will spread through a large part of the population. If $R_t\lt1$, the pandemic will slow quickly before it has a chance to infect many people. The lower the $R_0$: the more manageable the situation. In general, any $R_t\lt1$ means things are under control.

The value of $R_0$ helps us in two ways.

  1. It helps us understand how effective our measures have been controlling an outbreak
  2. it gives us vital information about whether we should increase or reduce restrictions based on our competing goals of economic prosperity and human safety. Well-respected epidemiologists argue that tracking Rt is the only way to manage through this crisis.

Limitations

Credits

Disclaimer

I’m not an Epidemiologist or scientist. I have only applied a modified version of a solution created by Bettencourt & Ribeiro 2008 to estimate real-time $R_0$ using a Bayesian approach. While this paper estimates a static R value, here we introduce a process model with Gaussian noise to estimate a time-varying $R_0$.

For a step by step calculation, please see this page

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