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EpiCURB: learning to derive epidemic control policies

EpiCURB: learning to derive epidemic control policies
EpiCURB: learning to derive epidemic control policies
The effectiveness of an epidemic control policy relies largely on how much effort is invested in every public health measure. Unfortunately, it is seldom possible to optimally allocate funds to these measures if the isolated effect of each intervention cannot be reliably estimated. We show how this challenge can be overcome by utilizing EpiCURB, a simulation-control framework that enables us to measure the effect of both untargeted and prioritized interventions on the epidemic outcome, where the latter are guided by reinforcement learning routines that effectively rank eligible individuals.
targeted testing, contact tracing, vaccination, reinforcement learning
1536-1268
57-62
Rusu, Andrei C.
901b9bc5-f776-4046-b694-e302c40c31b3
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Rusu, Andrei C.
901b9bc5-f776-4046-b694-e302c40c31b3
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Rusu, Andrei C., Farrahi, Katayoun and Niranjan, Mahesan (2024) EpiCURB: learning to derive epidemic control policies. IEEE Pervasive Computing, 3 (1), 57-62. (doi:10.1109/MPRV.2023.3329546).

Record type: Special issue

Abstract

The effectiveness of an epidemic control policy relies largely on how much effort is invested in every public health measure. Unfortunately, it is seldom possible to optimally allocate funds to these measures if the isolated effect of each intervention cannot be reliably estimated. We show how this challenge can be overcome by utilizing EpiCURB, a simulation-control framework that enables us to measure the effect of both untargeted and prioritized interventions on the epidemic outcome, where the latter are guided by reinforcement learning routines that effectively rank eligible individuals.

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EpiCURB_Accepted_IEEEPervasiveComputing - Accepted Manuscript
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Accepted/In Press date: 20 November 2023
Published date: 3 June 2024
Keywords: targeted testing, contact tracing, vaccination, reinforcement learning
Learn more about Vision, Learning and Control research Learn more about Institute for Life Sciences research Learn more about School of Electronics and Computer Science research Learn more about Institute for Life Sciences research

Identifiers

Local EPrints ID: 484685
URI: http://eprints.soton.ac.uk/id/eprint/484685
DOI: doi:10.1109/MPRV.2023.3329546
ISSN: 1536-1268
PURE UUID: dc854c4f-7a62-489a-aaa9-cdb26d003655
ORCID for Andrei C. Rusu: ORCID iD orcid.org/0000-0002-6053-1685
ORCID for Katayoun Farrahi: ORCID iD orcid.org/0000-0001-6775-127X
ORCID for Mahesan Niranjan: ORCID iD orcid.org/0000-0001-7021-140X

Catalogue record

Date deposited: 20 Nov 2023 17:42
Last modified: 15 Apr 2025 02:07

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Contributors

Author: Andrei C. Rusu ORCID iD
Author: Katayoun Farrahi ORCID iD
Author: Mahesan Niranjan ORCID iD

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