20.500.12556/RUL-168740 Optimizing non-pharmaceutical intervention strategies against COVID-19 using artificial intelligence 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. multi-objective optimization epidemiological modeling machine learning intervention plans večkriterijska optimizacija epidemiološko modeliranje intervencijski načrti true false true Angleški jezik Slovenski jezik Članek v reviji 2025-04-22 13:16:02 2025-04-22 13:16:08 2025-04-23 04:01:57 0000-00-00 00:00:00 2023 0 0 11 str. Vol. 11, art. 1073581 13 Feb. 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.8 2296-2565 10.3389/fpubh.2023.1073581 141456131 Janko_..._-Optimizing.pdf Janko_..._-Optimizing.pdf 1 CC8955705977F2BA5DB8DEC096B2E212 e61ba53694905911d077f5a812a78a5cfffa7fdae73a0c63c49c627459b1e80e 322f5324-1f6a-11f0-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=209639 https://www.frontiersin.org/articles/10.3389/fpubh.2023.1073581/full 1 0ea38edc-1f6a-11f0-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=209638 Fakulteta za strojništvo 0 0 0