The Signal and the Noise: Why So Many Predictions Fail-but Some Don't

by Nate Silver

The problem is that reliable estimates of R0 can usually not be formulated until well after a disease has swept through a community and there has been sufficient time to scrutinize the statistics. So epidemiologists are forced to make extrapolations about it from a few early data points. The other key statistical measure of a disease, the fatality rate, can similarly be difficult to measure accurately in the early going. It is a catch-22; a disease cannot be predicted very accurately without this information, but reliable estimates of these quantities are usually not available until the disease has begun to run its course. Instead, the data when an infectious disease first strikes is often misreported.