  Pathogen control is a critical aspect of modern poultry farming, providing
important benefits for both public health and productivity. Effective poultry
management measures to reduce pathogen levels in poultry flocks promote food
safety by lowering risks of food-borne illnesses. They also support animal
health and welfare by preventing infectious diseases that can rapidly spread
and impact flock growth, egg production, and overall health. This study frames
the search for optimal management practices that minimize the presence of
multiple pathogens as a combinatorial optimization problem. Specifically, we
model the various possible combinations of management settings as a solution
space that can be efficiently explored to identify configurations that
optimally reduce pathogen levels. This design incorporates a neural network
feedback-based method that combines feature explanations with global
sensitivity analysis to ensure combinatorial optimization in multiobjective
settings. Our preliminary experiments have promising results when applied to
two real-world agricultural datasets. While further validation is still needed,
these early experimental findings demonstrate the potential of the model to
derive targeted feature interactions that adaptively optimize pathogen control
under varying real-world constraints.
