How effective would a data-driven vaccination program be over a simple rules based approach The rollout of vaccination programs tends to be based on a given set of rules (advice) devised by government UK example. Such a rule-set are generally limited in complexity in order to be consistently applied by humans administering the roll-out.
There is a strong association between age and risk nature but it appears below the age of 60 that other factors such as sex/deprivation/ethenicity predominate. There is also mounting evidence towards mental disorders holding a significant risk factor risk factors and mental disorders
Is there any openly available papers or modelling showing a data-driven prioritization of vacination (for example using machine-intelligence), especially in the scenario of limited vaccine availability, would be more effective than simply using age alone as the predicate.
The reason for the rules based approach is the simpson paradox.

In short, the way a test group is sampled can easily overturn the conclusion of a study. It is very important to include sufficient patients from a particular risk group in the study.
This is why rules are set.
This approach however is the basis of causality theory. A data driven-approach would be as followed:

experts flag expected causal patterns, i.e. draw causal graph of the study. [This is the mathematical formulation of risk factors]
Causal graph generates ideal distribution of study and tools to draw conclusion