Conference Paper/Proceeding/Abstract 325 views
Addressing the Health Versus Economy Dilemma in Data-Driven Policymaking During a Pandemic
Proceedings of the Companion Conference on Genetic and Evolutionary Computation
Swansea University Authors:
Lewis Hotchkiss, Alma Rahat
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DOI (Published version): 10.1145/3583133.3590652
Abstract
The recent COVID-19 pandemic highlighted a need for tools to help policy-makers make informed decisions on what policies to implement in order to reduce the impact of the pandemic. Several tools have previously been developed to model how non-pharmaceutical interventions (NPIs), such as social dista...
| Published in: | Proceedings of the Companion Conference on Genetic and Evolutionary Computation |
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| ISBN: | 979-8-4007-0120-7 979-8-4007-0120-7 |
| Published: |
New York, NY, USA
ACM
2023
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| Online Access: |
http://dx.doi.org/10.1145/3583133.3590652 |
| URI: | https://cronfa.swan.ac.uk/Record/cronfa64015 |
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| Abstract: |
The recent COVID-19 pandemic highlighted a need for tools to help policy-makers make informed decisions on what policies to implement in order to reduce the impact of the pandemic. Several tools have previously been developed to model how non-pharmaceutical interventions (NPIs), such as social distancing, affect the rate of growth of a disease within a population. Much of the focus of the modelling effort have been on projections of health factors, relating them to the NPIs, with only few works addressing the health-economy trade-off. However, there is a particular gap in illustrations of real data-driven solutions in this area. In this paper, we proposed a purely data-driven framework where we modelled health and economic impacts with Bayesian and Recurrent Neural Network (RNN) models respectively, and used NSGA-II to identify policy stringencies over a three-week period. We demonstrate that this framework can produce a range of solutions trading off between health and economy projections based on real data, that may be used by policymakers to reach an informed decision. |
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| College: |
Faculty of Science and Engineering |