How to handle One Hot Encoded columns with changing categories in supervised ML Problem?
The main idea of Machine Learning is that your training data is similar to test data. If that hypothesis does not hold, then big chances of failure are guaranteed.
Given that, you still can handle unseen categories even thought the solution is not good and more with One Hot Encoding
In the category encoders library you have the following hyperparameter with the following options

handle_unknown: str
options are ‘error’, ‘return_nan’, ‘value’, and ‘indicator’. The default is ‘value’. Warning: if indicator is used, an extra column will be added in if the transform matrix has unknown categories. This can cause unexpected changes in dimension in some cases.

But, given your problem and your data I am not sure if you will ever be much more successful than doing some descriptive statistics.