I am an Associate Professor of engineering at Nanyang Technological University, Singapore. My research interests include modeling, simulation, and analysis of dynamical systems.

Much has been written about the ‘R’ number of coronavirus [1, 2, 3, 4]. R, or the “effective reproduction number”, is a way of measuring the ability of an epidemic to spread. Policymakers closely monitor it as they decide when to tighten social distancing measures or end lockdown.
The problem is, unlike other infection and fatality statistics that are widely reported, there are few sites we can go to look up the COVID-19 R number.
The purpose of this website is to provide a dashboard to track R. Now, I’m not an epidemiologist, just a concerned individual with enough technical background in math, stats, and data science to understand how to do it.
Note that there is much uncertainty in estimating the value of R at any point in time, and there are probably a dozen different ways of calculating the number. So you should take the estimates with a grain of salt. But I’m not too worried about the exact value, but rather the way it is trending. Despite the uncertainty, the different estimation methods are mostly consistent with each other in terms of trend and when it crosses the threshold of 1. Knowing when it goes above R = 1 informs us when we should take the disease seriously. (The “one” in the website url serves as a reminder.)
The dashboard presents the results for R for the past four months as a “heat map”. Red is when R is above 1, blue is when R is below 1, and white means R = 1. The size of the circles is proportional to the total number of infections. So big rings with red edges are the worst. It’s a good sign when all the circles on the map are deep blue in colour.