Knowledge is power. With knowledge, it is sometimes possible to affect outcomes. However, in order to affect an outcome, one must know early enough to undertake some action. The problem is timeliness; how to know early enough is a very important question.
In order to partially solve this problem, we often make estimates of what we think we know -- in colloquial English, we predict or forecast -- before we are certain. In this way, we hope to give ourselves time to act.
Prediction and forecast may seem like they mean the same
thing, but a quick spin around the Internet suggests that there is little or no consensus on what it is. Some fields use the terms differently; the climatologists, for example, have a nice set of working definitions that state clearly what is meant by the two terms.
But, for the case of making prospective predictions about outbreaks of infectious disease, especially if the predictions are made with the use of mathematical models, I think we should consider using the term "forecasting" for a couple of reasons.
First, whereas "prediction" tends to specify the time, location, and magnitude of an event (e.g., "We predict that a large outbreak of disease X will occur in region Y around date Z"), "forecasting" connotes a statistical statement (e.g., "We forecast that there is a 70% chance that a significant outbreak of disease X will occur in region Y around date Z"). In other words, "prediction" is a categorical statement that is either proven right or wrong (like predictions from a crystal ball), but "forecast" implies a probabilistic statement. "Forecast" conveys a degree of confidence. On the other hand, if we predict disease risk (see, e.g., Hay et al 2013), which is thought of as the probability of disease occurrence, then I think we're on solid footing.
Second, in some fields of science (e.g., physics), natural "laws" are known to incredible degrees of confidence. Given initial conditions to a specified accuracy, predictions can be made that are very reasonably believed. Unfortunately, infectious disease epidemiology is not one of those fields. Moreover, as George Box observed, "Essentially all models are wrong, but some are useful". Models tell us things about problems and allow us to do analysis in a systematic and (hopefully) objective way, but models are only models. They are not laws.
As Neils Bohr and others have noted, "Prediction is very difficult, especially about the future". We need to convey the finite confidence, whether high or low, in models to those using them.
(image source: ABE Books)