  In response to the COVID-19 pandemic and the potential threat of future
epidemics caused by novel viruses, we developed a flexible framework for
modeling disease intervention effects. This tool is intended to aid decision
makers at multiple levels as they compare possible responses to emerging
epidemiological threats for optimal control and reduction of harm. The
framework is specifically designed to be both scalable and modular, allowing it
to model a variety of population levels, viruses, testing methods and
strategies--including pooled testing--and intervention strategies. In this
paper, we provide an overview of this framework and examine the impact of
different intervention strategies and their impact on infection dynamics.
