The model isn't predictive though - it's a simulator. If we'd waited until we had enough data to make predictions with it (which I doubt you could given the sheer number of parameters) it'd be too late to use any of the interventions.
How would you ethically collect training data for the interventions?
The outputs of the model _were_ being treated as predictions.
The Ferguson paper from 16 March used the language of prediction: "In the (unlikely) absence of any control measures [...] given an estimated R0 of 2.4, we predict 81% of the GB and US populations would be infected over the course of the epidemic." [1]. The news coverage also used that language: "Imperial researchers model likely impact of public health measures" [2]. And look at the rest of the comments in this discussion, and count how many types "predict" appears!
> If we'd waited until we had enough data to make predictions with it
This is like the drunk looking for their keys under a streetlight. "Did you lose the keys here?" "No, but the light is much better here." -- "How confident are you in your model's predictions?" "I have no idea, but it's the model I have."
Also -- the Ferguson model made predictions, based on the parameters they picked. You don't need to wait for data to make predictions; you only need data to validate your predictions.
> How would you ethically collect training data for the interventions?
You don't. You (as a scientist who influences public policy) should publish validated confidence intervals for your predictions. You (as a government) should understand that there is a huge margin of uncertainty in the predictions, and accept that sometimes you just have to make decisions in the absence of knowledge. You (both the scientist and the government) do not go around spouting "Our decisions are led by science".
How would you ethically collect training data for the interventions?