The COVID-19 pandemic has led to a wealth of research studies examining various aspects of the pandemic. In this post, I will discuss some of the available datasets for doing aggregate level analysis, i.
This tutorial gives an overview of the COVID-19 policy indexes just released by the CoronaNet project of which I am a part and the Oxford Government Response Tracker.
While perusing the news, I read eagerly about the CDC’s recent study examining associations between county-level mask mandates and COVID-19 growth rates. This study has already been the subject of angry retorts from the restaurant industry due to the CDC’s claim that restaurant closures reduced COVID-19 spread.
In this blog post, I use Gelman and Loken’s garden of forking paths analysis to construct a simulation showing why skepticism of AstraZeneca’s vaccine results is warranted at this early stage.
I start with Eric Novik’s excellent blog post on how to calculate the relevant statistics for the vaccine, i.e. vaccine efficacy (VE). This is defined as:
\[ VE = 1 - \frac{p_t}{p_c} \]