Optimizing non-pharmaceutical intervention strategies against Covid-19 using artificial intelligence
Bibliographic Data
| ID | 22066680 |
|---|---|
| Authors | Vito Janko (0000-0002-9549-9742, Jožef Stefan Institute, corresponding author), Nina Reščič (0000-0001-5428-3577, Jožef Stefan Institute), Aljoša Vodopija (0000-0003-0299-4160, Jožef Stefan Institute), David Susič (0000-0003-0920-1952, Jožef Stefan Institute), Carlo De Masi (Jožef Stefan Institute), Tea Tušar (0000-0002-6495-006X, Jožef Stefan Institute), Anton Gradišek (0000-0001-6480-9587, Jožef Stefan Institute), Sophie Vandepitte (0000-0002-6965-6775, Ghent University), Delphine De Smedt (0000-0001-7035-8508, Ghent University), Jana Javornik (0000-0002-6103-1359, University of Leeds), Matjaž Gams (0000-0002-5747-0711, Jožef Stefan Institute), Mitja Luštrek (0000-0003-3219-2935, Jožef Stefan Institute, corresponding author) |
| Year | 2023 |
| Volume | 11 |
| Pages | 1073581-1073581 |
| Publication date | 2023-02-13 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2023.1073581 |
| PMID | 36860399 |
| OpenAlex | W4320495883 |
| Language | EN |
| Citations received | 2 |
| References cited | 22 |
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
Economics · Geography · Machine learning · Pandemic · Psychological intervention · Computer Science · COVID-19 epidemiological studies · COVID-19 Pandemic Impacts · Medicine · Nursing · SARS-CoV-2 and COVID-19 Research · Artificial Intelligence
Estimating the effects of non-pharmaceutical interventions on Covid-19 in Europe
A fast and elitist multiobjective genetic algorithm
Machine Learning for Analyzing Non-Countermeasure Factors Affecting Early Spread of Covid-19
A global panel database of pandemic policies (Oxford Covid-19 Government Response Tracker)
Ranking the effectiveness of worldwide Covid-19 government interventions
A global database of Covid-19 vaccinations
| Citations per year | 0.67 |
|---|---|
| Citation span | 2023 - 2024 (2) |
| Highly cited | No |