The Lee-Carter method for modeling and forecasting mortality has been
shown to work quite well given long time series of data. Here we consider
how it can be used when there are few observations at uneven intervals.
Assuming that the underlying model is correct and that the mortality index
follows a random walk with drift, we find the method can be used with
sparse data. The central forecast depends mainly on the first and last
observation, and so can be generated with just two observations,
preferably not too close in time. With three data points, uncertainty can
also be estimated, although such estimates of uncertainty are themselves
highly uncertain and improve with additional observations. We apply the
methods to China and South Korea, which have 3 and 20 data points,
respectively, at uneven intervals.