Learning to Act: Novel Integration of Algorithms and Models for Epidemic Preparedness
This work addresses the challenge of evidence-based decision-making for stakeholders in epidemic planning, though it appears incremental as it focuses on coupling existing models and algorithms rather than developing new ones.
The paper tackles the problem of epidemic preparedness by introducing a framework that integrates epidemiological models with machine learning algorithms to aid decision-making, demonstrated in the context of the COVID-19 pandemic, with contributions including a novel platform for stakeholder interaction and open-source release under the Apache-2.0 License.
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.