Learning to Act: Novel Integration of Algorithms and Models for Epidemic Preparedness

Abstract

In this work we present a framework which will transform research and praxis in epidemic planning. Introduced in the context of the ongoing COVID-19 pandemic, we provide a concrete demonstration of the ways that algorithms can be integrated with epidemiological models to amplify their value. Our contribution in this work is two fold: 1) a novel platform which makes it easy for stakeholders to interact with epidemiological models and algorithms developed within the 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.