Janko, V, Reščič, N, Vodopija, A et al. (9 more authors) (2023) Optimizing non-pharmaceutical intervention strategies against COVID-19 using artificial intelligence. Frontiers in Public Health, 11. 1073581. ISSN 2296-2565
Abstract
One key task in the early fight against the COVID-19 pandemic was to plan non-pharmaceutical interventions to reduce the spread of the infection while limiting the burden on the society and economy. With more data on the pandemic being generated, it became possible to model both the infection trends and intervention costs, transforming the creation of an intervention plan into a computational optimization problem. This paper proposes a framework developed to help policy-makers plan the best combination of non-pharmaceutical interventions and to change them over time. We developed a hybrid machine-learning epidemiological model to forecast the infection trends, aggregated the socio-economic costs from the literature and expert knowledge, and used a multi-objective optimization algorithm to find and evaluate various intervention plans. The framework is modular and easily adjustable to a real-world situation, it is trained and tested on data collected from almost all countries in the world, and its proposed intervention plans generally outperform those used in real life in terms of both the number of infections and intervention costs.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 Janko, Reščič, Vodopija, Susič, De Masi, Tušar, Gradišek, Vandepitte, De Smedt, Javornik, Gams and Luštrek. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
Keywords: | COVID-19, multi-objective optimization, epidemiological modeling, machine learning, intervention plans |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Work and Employment Relation Division (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 15 Feb 2023 14:00 |
Last Modified: | 25 Jun 2023 23:14 |
Status: | Published |
Publisher: | Frontiers Media |
Identification Number: | 10.3389/fpubh.2023.1073581 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:195949 |