Known Unknowns: Coronavirus Models
Sometimes a picture is worth a thousand question marks.
If you noticed the dramatic variability in the forecast, you’re not alone. The point isn’t lost on the article’s authors at The Times, either. To wit:
Or, perhaps even more on the nose:
First, the disclosures: I’m not a statistician or an epidemiologist (obviously) — so I can’t vouch for the precision of the differential equations or how well the outputs correlate to real-world disease outcomes.
However, assuming the calculations are a first-order approximation* of the model from The Lancet the author cites, the point is this: The calculator model exhibits extreme sensitivity to even minuscule variations in user input.
Push one input slider a bit to the right, and the curve grows dramatically; ooch another input slider a tiny bit to the left, and the curve shrinks dramatically.
Moreover, combining the categories of Transmission Dynamics and Clinical Dynamics results in eleven independent variables being modeled — with most, in various combinations, exhibiting dramatic curve-shifting properties.
What makes a visual model like this so useful — particularly to those of us who aren’t epidemiologists — is that it clearly and simply conveys an essential truth: Namely, that the scientists who are attempting to forecast the path of this virus face a truly daunting challenge.
*NOTE: Real Vision attempted to contact the author of the Epidemic Calculator to verify the source code and underlying formulas but was unable to reach him. Real Vision was, however, able to independently confirm his status as a researcher at OpenAI, a well-known research laboratory in San Francisco, which focuses on artificial intelligence.