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COVID-19

I really did not expect 2020 to go this way. I thought it would be quite different. Maybe you’ve been living under a rock, so I should catch you up briefly on what’s happened in 2020. In December of last year, the city of Wuhan suffered a serious coronavirus outbreak. Though China attempted to contain it, the virus is quite contagious and by late January of 2020, outbreaks were seeded in major cities around the world.

What follows is a brief academic exercise meant to illustrate the epidemiology underlying the SARS-COV-2 outbreak. In later posts, I may delve more deeply into the biology of the disease, but this post is limited to an exploration of the viral reproductive number, known as R0 (R-nought), associated with SARS-Cov-2, and what sort of inferences we can make by calculating this number. The jupyter notebooks is below, but please keep in mind that this blog post is presented exclusively as an exercise, makes NO attempt to be particularly accurate, and claims in this notebook should NOT be considered primary literature material on their own. For the most up-to-date, relevant and accurate information out there, I defer to the CDC and to the Nextstrain team. With that said, I hope the blog post helps people understand how some of this works.

A last brief note: In the notebook below, I did not attempt to fit a SIR model to the data, and instead opted for an exponential approximation. The reason for this was primarily clarity and simplicity. At this early point in the pandemic, an exponential approximation is quite valid. I have also stopped including data after a certain date, so that approximations do not take into account the effects of social distancing.

Jupyter Notebook

Written on April 12, 2020