Optimizing non-pharmaceutical intervention strategies against COVID-19 using artificial intelligence

Janko, Vito (author), Reščič, Nina (author), Vodopija, Aljoša (author), Susič, David (author), De Masi, Carlo (author), Tušar, Tea (author), Gradišek, Anton (author), Vandepitte, Sophie (author), De Smedt, Delphine (author), Javornik, Jana S. (author), Gams, Matjaž (author), Luštrek, Mitja (author)

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.

• Language: English
• Keywords: multi-objective optimization, epidemiological modeling, machine learning, intervention plans
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FS - Faculty of Mechanical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2023
• Number of pages: 11 str.
• Numbering: Vol. 11, art. 1073581
• PID: 20.500.12556/RUL-168740
• UDC: 004.8
• ISSN on article: 2296-2565
• DOI: 10.3389/fpubh.2023.1073581
• COBISS.SI-ID: 141456131
• Publication date in RUL: 22.04.2025

Record is a part of a journal

• Title: Frontiers in public health
• Shortened title: Front. public health
• Publisher: Frontiers Media
• ISSN: 2296-2565
• COBISS.SI-ID: 523096857

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: večkriterijska optimizacija, epidemiološko modeliranje, intervencijski načrti

Projects

• Funder: ARIS - Slovenian Research and Innovation Agency
• Project number: P2-0209-2022
• Name: Umetna inteligenca in inteligentni sistemi