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

TL;DR

This paper introduces a framework that integrates machine learning algorithms with epidemiological models to enhance epidemic preparedness and decision-making, demonstrated through COVID-19 context.

Contribution

It presents a new platform enabling stakeholders to interact with models and algorithms, fostering better epidemic planning and evidence-based decisions.

Findings

A novel platform for epidemic modeling interaction

Coupling algorithms with epidemiological models

Open-source release under Apache-2.0 License

Abstract

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.