  Policy decisions are increasingly dependent on the outcomes of simulations
and/or machine learning models. The ability to share and interact with these
outcomes is relevant across multiple fields and is especially critical in the
disease modeling community where models are often only accessible and workable
to the researchers that generate them. This work presents a blockchain-enabled
system that establishes a decentralized trust between parties involved in a
modeling process. Utilizing the OpenMalaria framework, we demonstrate the
ability to store, share and maintain auditable logs and records of each step in
the simulation process, showing how to validate results generated by computing
workers. We also show how the system monitors worker outputs to rank and
identify faulty workers via comparison to nearest neighbors or historical
reward spaces as a means of ensuring model quality.
