ArticleEuropean journal of operational research2023
Stochastic optimization for vaccine and testing kit allocation for the COVID-19 pandemic.
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 17 papers, 1 of them a synthesis that pooled it.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Optimizing patient flow logistics: strategic challenges, tactical solutions, and future directions.BMC health services research · 2025Pooled it
- Optimizing vaccine site locations while considering travel inconvenience and public health outcomes.Health care management science · 2026Article
- Optimizing vaccine allocation: a review of epidemiological models with behavioral adaptation.Clinical and experimental vaccine research · 2026Review
- Can past variants of SARS-CoV-2 predict the impact of future variants? Machine learning for early warning of US counties at risk.Health care management science · 2025Article
- Reinforcement learning for healthcare operations management: methodological framework, recent developments, and future research directions.Health care management science · 2025Review
- Optimization Modeling for Pandemic Vaccine Supply Chain Management: A Review and Future Research Opportunities.Naval research logistics · 2024Article
- Analyzing economic effect on mRNA vaccine inventory management with redistribution policy.Scientific reports · 2024Article
- Covid19Vaxplorer: A free, online, user-friendly COVID-19 vaccine allocation comparison tool.PLOS global public health · 2024Article
- Fair-split distribution of multi-dose vaccines with prioritized age groups and dynamic demand: The case study of COVID-19.European journal of operational research · 2023Article
- Risk-based allocation of COVID-19 personal protective equipment under supply shortages.European journal of operational research · 2023Article
- Behavioral Analytics for Myopic Agents.European journal of operational research · 2023Article
- Optimal timing of non-pharmaceutical interventions during an epidemic.European journal of operational research · 2023Article
- Designing an optimization model for the vaccine supply chain during the COVID-19 pandemic.Expert systems with applications · 2023Article
- A new vaccine supply chain network under COVID-19 conditions considering system dynamic: Artificial intelligence algorithms.Socio-economic planning sciences · 2023Article
- Introduction to the special issue on the role of operational research in future epidemics/ pandemics.European journal of operational research · 2023Article
- Social equity-based distribution networks design for the COVID-19 vaccine.International journal of production economics · 2022Article
- Optimal control of the spatial allocation of COVID-19 vaccines: Italy as a case study.PLoS computational biology · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a formal mathematical modeling framework for a multi-agent sequential decision problem during an epidemic. The problem is formulated as a collaboration between a vaccination agent and learning agent to allocate stockpiles of vaccines and tests to a set of zones under various types of uncertainty. The model is able to capture passive information processes and maintain beliefs over the uncertain state of the world. We designed a parameterized direct lookahead approximation which is robust and scalable under different scenarios, resource scarcity, and beliefs about the environment. We design a test allocation policy designed to capture the value of information and demonstrate that it outperforms other learning policies when there is an extreme shortage of resources (information is scarce). We simulate the model with two scenarios including a resource allocation problem to each state in the United States and another for the nursing homes in Nevada. The US example demonstrates the scalability of the model and the nursing home example demonstrates the robustness under extreme resource shortages.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.