Learning to Act: Novel Integration of Algorithms and Models for Epidemic Preparedness. (arXiv:2210.02055v1 [cs.LG])

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 …

algorithms arxiv epidemic integration