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Addressing the Health Versus Economy Dilemma in Data-Driven Policymaking During a Pandemic

Lewis Hotchkiss, Alma Rahat Orcid Logo

Proceedings of the Companion Conference on Genetic and Evolutionary Computation

Swansea University Authors: Lewis Hotchkiss, Alma Rahat Orcid Logo

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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...

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Published in: Proceedings of the Companion Conference on Genetic and Evolutionary Computation
ISBN: 979-8-4007-0120-7 979-8-4007-0120-7
Published: New York, NY, USA ACM 2023
Online Access: http://dx.doi.org/10.1145/3583133.3590652
URI: https://cronfa.swan.ac.uk/Record/cronfa64015
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first_indexed 2023-09-18T10:31:11Z
last_indexed 2023-09-18T10:31:11Z
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spelling v2 64015 2023-08-02 Addressing the Health Versus Economy Dilemma in Data-Driven Policymaking During a Pandemic 6a7ca50c64ba8d92a892a93379d8f25b Lewis Hotchkiss Lewis Hotchkiss true false 6206f027aca1e3a5ff6b8cd224248bc2 0000-0002-5023-1371 Alma Rahat Alma Rahat true false 2023-08-02 HDAT 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. Conference Paper/Proceeding/Abstract Proceedings of the Companion Conference on Genetic and Evolutionary Computation ACM New York, NY, USA 979-8-4007-0120-7 979-8-4007-0120-7 15 7 2023 2023-07-15 10.1145/3583133.3590652 http://dx.doi.org/10.1145/3583133.3590652 COLLEGE NANME Health Data Science COLLEGE CODE HDAT Swansea University 2023-11-20T15:13:20.0782287 2023-08-02T14:53:01.4033910 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Lewis Hotchkiss 1 Alma Rahat 0000-0002-5023-1371 2
title Addressing the Health Versus Economy Dilemma in Data-Driven Policymaking During a Pandemic
spellingShingle Addressing the Health Versus Economy Dilemma in Data-Driven Policymaking During a Pandemic
Lewis Hotchkiss
Alma Rahat
title_short Addressing the Health Versus Economy Dilemma in Data-Driven Policymaking During a Pandemic
title_full Addressing the Health Versus Economy Dilemma in Data-Driven Policymaking During a Pandemic
title_fullStr Addressing the Health Versus Economy Dilemma in Data-Driven Policymaking During a Pandemic
title_full_unstemmed Addressing the Health Versus Economy Dilemma in Data-Driven Policymaking During a Pandemic
title_sort Addressing the Health Versus Economy Dilemma in Data-Driven Policymaking During a Pandemic
author_id_str_mv 6a7ca50c64ba8d92a892a93379d8f25b
6206f027aca1e3a5ff6b8cd224248bc2
author_id_fullname_str_mv 6a7ca50c64ba8d92a892a93379d8f25b_***_Lewis Hotchkiss
6206f027aca1e3a5ff6b8cd224248bc2_***_Alma Rahat
author Lewis Hotchkiss
Alma Rahat
author2 Lewis Hotchkiss
Alma Rahat
format Conference Paper/Proceeding/Abstract
container_title Proceedings of the Companion Conference on Genetic and Evolutionary Computation
publishDate 2023
institution Swansea University
isbn 979-8-4007-0120-7
979-8-4007-0120-7
doi_str_mv 10.1145/3583133.3590652
publisher ACM
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url http://dx.doi.org/10.1145/3583133.3590652
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description 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.
published_date 2023-07-15T15:13:21Z
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