Optimal design of vaccination policies: A case study for Newfoundland and Labrador

IF 0.8 4区管理学 Q4 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Letters Pub Date : 2024-07-01 DOI:10.1016/j.orl.2024.107140

Faraz Khoshbakhtian , Hamidreza Validi , Mario Ventresca , Dionne Aleman

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引用次数: 0

Abstract

This paper proposes pandemic mitigation vaccination policies for Newfoundland and Labrador (NL) based on two compact mixed integer programming (MIP) models of the distance-based critical node detection problem (DCNDP). Our main focus is on two variants of the DCNDP that seek to minimize the number of connections with lengths of at most one (1-DCNDP) and two (2-DCNDP). A polyhedral study for the 1-DCNDP is conducted, and new aggregated inequalities are provided for the 2-DCNDP. The computational experiments show that the 2-DCNDP with aggregated inequalities outperforms the one with disaggregated inequalities for graphs with a density of at least 0.5%. We also study the strategic vaccine allocation problem as a real-world application of the DCNDP and conduct a set of computational experiments on a simulated contact network of NL. Our computational results demonstrate that the DCNDP-based strategies can have a better performance in comparison with the real-world strategies implemented during COVID-19.

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疫苗接种政策的优化设计：纽芬兰和拉布拉多案例研究

本文基于基于距离的关键节点检测问题（DCNDP）的两个紧凑型混合整数编程（MIP）模型，为纽芬兰和拉布拉多（NL）提出了大流行病缓解疫苗接种政策。我们的主要重点是 DCNDP 的两个变体，它们分别寻求最大限度减少长度为 1（1-DCNDP）和 2（2-DCNDP）的连接数。我们对 1-DCNDP 进行了多面体研究，并为 2-DCNDP 提供了新的集合不等式。计算实验表明，对于密度至少为 0.5% 的图，采用聚合不等式的 2-DCNDP 优于采用分解不等式的 2-DCNDP。我们还将战略疫苗分配问题作为 DCNDP 在现实世界中的应用进行了研究，并在 NL 的模拟接触网络上进行了一系列计算实验。我们的计算结果证明，与 COVID-19 期间实施的真实世界策略相比，基于 DCNDP 的策略具有更好的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Operations Research Letters 管理科学-运筹学与管理科学

CiteScore

2.10

自引率

9.10%

发文量

111

审稿时长

83 days

期刊介绍： Operations Research Letters is committed to the rapid review and fast publication of short articles on all aspects of operations research and analytics. Apart from a limitation to eight journal pages, quality, originality, relevance and clarity are the only criteria for selecting the papers to be published. ORL covers the broad field of optimization, stochastic models and game theory. Specific areas of interest include networks, routing, location, queueing, scheduling, inventory, reliability, and financial engineering. We wish to explore interfaces with other fields such as life sciences and health care, artificial intelligence and machine learning, energy distribution, and computational social sciences and humanities. Our traditional strength is in methodology, including theory, modelling, algorithms and computational studies. We also welcome novel applications and concise literature reviews.