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Computer Science > Machine Learning

arXiv:2210.02055 (cs)
COVID-19 e-print

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[Submitted on 5 Oct 2022]

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

Authors:Sekou L. Remy, Oliver E. Bent
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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.
Comments: Presented at the ICLR 2021 Workshop on AI for Public Health. arXiv admin note: substantial text overlap with arXiv:2111.07779
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2210.02055 [cs.LG]
  (or arXiv:2210.02055v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2210.02055

Submission history

From: Sekou Remy [view email]
[v1] Wed, 5 Oct 2022 07:13:56 UTC (859 KB)
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