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

by Nate Silver

Although the statistical methods that epidemiologists use when a flu outbreak is first detected are not quite as simple as the preceding examples, they still face the challenge of making extrapolations from a small number of potentially dubious data points. One of the most useful quantities for predicting disease spread is a variable called the basic reproduction number. Usually designated as R0, it measures the number of uninfected people that can expect to catch a disease from a single infected individual. An R0 of 4, for instance, means that—in the absence of vaccines or other preventative measures—someone who gets a disease can be expected to pass it along to four other individuals before recovering (or dying) from it. In theory, any disease with an R0 greater than 1 will eventually spread to the entire population in the absence of vaccines or quarantines. But the numbers are sometimes much higher than this: R0 was about 3 for the Spanish flu, 6 for smallpox, and 15 for measles. It is perhaps well into the triple digits for malaria, one of the deadliest diseases in the history of civilization, which still accounts for about 10 percent of all deaths in some parts of the world today.