Saw this tweet today:
Very interesting graph of the Ebola outbreak. David put together data on the total number of cases, total deaths, and numbers for Liberia, Sierra Leone, and Guinea. His graph is here.
It’s honestly terrifying. Reading numbers in a news story is one thing, but seeing the slope of that graph makes it real for me. Ebola is not messing around.
I didn’t do much, but added an exponential model with the growth rate. It’s pretty scary to see a 4% rate of daily growth in Liberia, and to look at where the numbers are in a few months. My graph is here.
It’s sobering to create a graph about data that’s so real. People are dying, every day, and they’re dying not only because of a deadly disease but because the world has not been able to mobilize enough resources to contain the disease.
I wonder if this will help.
I won’t be teaching this lesson until about January — I’ve been blogging up a storm recently about exponential growth and decay because awesome data sets keep falling into my lap, but it just doesn’t fit this early in the year — I’ll be introducing linear functions in about two weeks, and my school’s scope and sequence dictate quadratics before exponential functions. But waiting a few months will provide a thought-provoking case study: the model predicts, being a bit conservative, about 100,000 cases and 50,000 deaths by mid-January (obviously some room for debate here). I really hope that that is not the case — and it provides a valuable lesson in the limitations of the models we use — math is powerful, but it is also imperfect.
I hope that we can solve Ebola, and that in January, Act 3 of this lesson is a mass-produced vaccine and the logistics of a successful campaign to get that vaccine to everyone who needs it.