In the post on estimating the rate of spread in the current ebola epidemic, a commenter stated that using a monthly rate of disease spread in Liberia was a “simpler” model than what I had done, which was based on a daily rate. This is not correct and I want to clarify why here.

In fact I used a very simple model–an exponential model, which has the form *y = b^ax*. You can’t get any simpler than a one parameter model, and that fact doesn’t change just because you alter the value of the base *b*. *Any* base can model an exponential increase; changing it just requires a corresponding change in parameter *a*, for a given pair of y and x variables. Base choice ought to be done in a way that carries some meaning. For example, if you’re inherently interested in the doubling time of something, then 2 is the logical choice*. But when no particular base value is obvious, it’s still best if the value used carries meaning in terms of the values of x, i.e. where a = 1.0, presuming that x is measured on some scale that has inherent interest. In my case, that’s the per-day increase in ebola cases.

However, if you fit an exponential model to some data, most programs will use a base of *e* (~2.781) or 10 by default; the base is fixed and the rate of change is then determined with respect to the units of ax. That’s a bit backwards frankly, but not a big deal, because the base used can easily be converted to whatever base *is* more meaningful relative to the data at hand. Say for example, that your model fitting procedure gives y = e^(3.2x), where b = e and a = 3.2. But if your x variable is recorded in say, days, you may well not be interested in how y changes every 3.2 days: you want to know the *per-day* rate of change. Well, y = e^(ax) is simply y = (e^a)^x, and so in this case b = e^(3.2) = 24.5; it takes a larger base to return a given y value if the exponent is smaller. It’s just a straight mathematical transformation (e^a), where *a* is whatever value is returned in the exponential model fitting. It has nothing to do with model complexity. It has instead to do with scaling, ease of interpretation and convenience.

The relevance to the ebola transmission rate modeling and the original comment is that those rates could very well change within a month’s time due to radical changes in the population’s behavior (critical), or perhaps drug availability (unlikely in this case). In a disease epidemic what happens from day to day is critical. So you want to use a time scale that allows you to detect system changes quickly, while (in this case) also acknowledging the noise generated by the data reporting process (which complicates things and was the whole point of using loess to smooth the raw data before making the estimates). Note that I’ve not gone into the issue of how to detect when an exponential growth rate has changed to some other type of growth. That’s much more difficult.

*Exponential functions are also useful for analyzing outcomes of trials with categorical variables, *a* where a = 1 and b defines the number of possible outcomes of some repeated process. For example y = 2^25 gives the total number of possible permutations of 25 trials of an event having two possible outcomes. But that’s a different application than modeling a change rate (unless you want to consider the increase in the number of possible permutations a rate).