ArticleFrontiers in public health2023
Optimizing non-pharmaceutical intervention strategies against COVID-19 using artificial intelligence.
Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges.Nature communications · 2025Article
- Balancing health and socioeconomic impacts: A uniform framework for evaluating non-pharmaceutical interventions.PloS one · 2025Article
- Is SARS-CoV-2 elimination or mitigation best? Regional and disease characteristics determine the recommended strategy.Royal Society open science · 2024Article
- Assessing the impact of isolation policies on epidemic dynamics through swarm entropy.Frontiers in public health · 2024Article
- Predicting COVID-19 pandemic waves including vaccination data with deep learning.Frontiers in public health · 2023Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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