  Forecasting influenza like illnesses (ILI) has rapidly progressed in recent
years from an art to a science with a plethora of data-driven methods. While
these methods have achieved qualified success, their applicability is limited
due to their inability to incorporate expert feedback and guidance
systematically into the forecasting framework. We propose a new approach
leveraging the Seldonian optimization framework from AI safety and demonstrate
how it can be adapted to epidemic forecasting. We study two types of guidance:
smoothness and regional consistency of errors, where we show that by its
successful incorporation, we are able to not only bound the probability of
undesirable behavior to happen, but also to reduce RMSE on test data by up to
17%.
