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

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

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.

• Jezik: Angleški jezik
• Ključne besede: multi-objective optimization, epidemiological modeling, machine learning, intervention plans
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FS - Fakulteta za strojništvo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2023
• Št. strani: 11 str.
• Številčenje: Vol. 11, art. 1073581
• PID: 20.500.12556/RUL-168740
• UDK: 004.8
• ISSN pri članku: 2296-2565
• DOI: 10.3389/fpubh.2023.1073581
• COBISS.SI-ID: 141456131
• Datum objave v RUL: 22.04.2025

Gradivo je del revije

• Naslov: Frontiers in public health
• Skrajšan naslov: Front. public health
• Založnik: Frontiers Media
• ISSN: 2296-2565
• COBISS.SI-ID: 523096857

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: večkriterijska optimizacija, epidemiološko modeliranje, intervencijski načrti

Projekti

• Financer: ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
• Številka projekta: P2-0209-2022
• Naslov: Umetna inteligenca in inteligentni sistemi