  In this work we present a framework which may transform research and praxis
in epidemic planning. Introduced in the context of the ongoing COVID-19
pandemic, we provide a concrete demonstration of the way algorithms may learn
from epidemiological models to scale their value for epidemic preparedness. Our
contributions in this work are two fold: 1) a novel platform which makes it
easy for decision making stakeholders to interact with epidemiological models
and algorithms developed within the Machine learning community, and 2) the
release of this work under the Apache-2.0 License. The objective of this paper
is not to look closely at any particular models or algorithms, but instead to
highlight how they can be coupled and shared to empower evidence-based decision
making.
