ArticleEuropean journal of operational research2023
On the impact of resource relocation in facing health emergencies.
Article in European journal of operational research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- A stochastic programming approach for dynamic allocation of bed capacity and assignment of patients to collaborating hospitals during pandemic outbreaks.Health care management science · 2026Article
- An account of the healthcare-seeking behavior of the public in response to a healthcare emergency (coronavirus disease 2019): The Saudi community experience.Journal of family & community medicineArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The outbreak of SARS-CoV-2 and the corresponding surge in patients with severe symptoms of COVID-19 put a strain on health systems, requiring specialized material and human resources, often exceeding the locally available ones. Motivated by a real emergency response system employed in Northern Italy, we propose a mathematical programming approach for rebalancing the health resources among a network of hospitals in a large geographical area. It is meant for tactical planning in facing foreseen peaks of patients requiring specialized treatment. Our model has a clean combinatorial structure. At the same time, it considers the handling of patients by a dedicated home healthcare service, and the efficient exploitation of resource sharing. We introduce mathematical programming heuristic based on decomposition methods and column generation to drive very large-scale neighborhood search. We evaluate its embedding in a multi-objective optimization framework. We experiment on real world data of the COVID-19 in Northern Italy during 2020, whose aggregation and post processing is made openly available to the community. Our approach proves to be effective in tackling realistic instances, thus making it a reliable basis for actual decision support tools.
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