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Scientists, we're only as good as our ability to communicate data to the public, so here goes my best shot. This graph is data from the Pfizer "BNT162b2 mRNA" Covid-19 Vaccine clinical trial. (from Polack, F. P.; et al. N. Engl. J. Med. 2020, NEJMoa2034577) 1/6
This graph shows the occurrence of COVID-19 among patients after they were given one dose of the vaccine. The y-axis is the % of the patients in the trial group who got COVID, the x-axis is the days after the dose was given. A steeper line means a less effective treatment.. 2/6
so we want the flattest line (ideally at 0%) possible. In this case, the red line is the vaccine treatment group, where the blue line is the "placebo" (aka no treatment) group. Now these lines look very different, which at a first glance appears to suggest good treatment.... 3/6
but we as scientists don't just want "looks good," we want stats. In particular, an efficacy confidence interval, aka a range of numbers within which we are 95% confident that vaccine efficacy will fall. This is based on the avg percent occurrence of each group, 4/6
the number of patients within each group, and the standard deviation (the spread of the data) within each group. Now based on the paper this clinical data comes from, the range of vaccine efficacy (as a %) after just the first does is 75.6-86.9%. 5/6
This is *awesome* for a vaccine. And, after a second dose of the vaccine, the 95% confidence interval of the efficacy jumps up to 89.8-97.6%. Hopefully this helps explain a graph that's been circling around the twitterverse/news. 6/6 
