I personally wouldn't call this "data cleaning". I think of data cleaning more in the sense of data editing - cleaning up inconsistencies in the data set (e.g. a record has reported age of 1000, or a person aged 4 is a single parent, etc.).
The presence of a real effect in your data does not make it "messy" (to the contrary, the presence of real effects would make it rich) - although it can make your mathematical task more involved. I would suggest that the data be "cleaned" in this way if it is the only feasible way to get a prediction. If there is a feasible way which doesn't throw away information, then use that.
It sounds like you may benefit from some sort of cyclical analysis, given that you say this effect comes around periodically (kind of like a "business cycle").
From my point of view, if you are looking at forecasting something, then removing a genuine effect from that source can only make your predictions worse. This is because you have effectively "thrown away" the very information that you wish to predict!
The other point is that it may be difficult to determine how much of a set of deaths were due to the epidemic, and how much was caused by the ordinary fluctuations.
In statistical terminology, the epidemic sounds like that, from your point of view, it is a "nuisance" to what you actually want to analyse. So you aren't particularly interested in it, but you need to somehow account for it in your analysis. One "quick and dirty" way to do this in a regression setting is to include an indicator for the epidemic years/periods as a regressor variable. This will give you an average estimate of the effect of epidemics (and implicitly assumes the affect is the same for each epidemic). However, this approach only works for describing the effect, because in forecasting, your regression variable is unknown (you don't know which periods in the future will be epidemic ones).
Another way to account for the epidemic is to use a mixture model with two components: one model for the epidemic part and one model for the "ordinary" part. The model then proceeds in two steps: 1) classify an period as epidemic or normal, then 2) apply the model to which it was classified.