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菲律宾市级层面的 COVID-19 疫苗接种点的最优选择。

Optimal selection of COVID-19 vaccination sites in the Philippines at the municipal level.

机构信息

Institute of Mathematics, University of the Philippines Diliman, Quezon City, Philippines.

出版信息

PeerJ. 2022 Sep 30;10:e14151. doi: 10.7717/peerj.14151. eCollection 2022.

Abstract

In this work, we present an approach to determine the optimal location of coronavirus disease 2019 (COVID-19) vaccination sites at the municipal level. We assume that each municipality is subdivided into smaller administrative units, which we refer to as barangays. The proposed method solves a minimization problem arising from a facility location problem, which is formulated based on the proximity of the vaccination sites to the barangays, the number of COVID-19 cases, and the population densities of the barangays. These objectives are formulated as a single optimization problem. As an alternative decision support tool, we develop a bi-objective optimization problem that considers distance and population coverage. Lastly, we propose a dynamic optimization approach that recalculates the optimal vaccination sites to account for the changes in the population of the barangays that have completed their vaccination program. A numerical scheme that solves the optimization problems is presented and the detailed description of the algorithms, which are coded in Python and MATLAB, are uploaded to a public repository. As an illustration, we apply our method to determine the optimal location of vaccination sites in San Juan, a municipality in the province of Batangas, in the Philippines. We hope that this study may guide the local government units in coming up with strategic and accessible plans for vaccine administration.

摘要

在这项工作中,我们提出了一种在市级确定 2019 年冠状病毒病(COVID-19)疫苗接种点最佳位置的方法。我们假设每个直辖市都被细分为更小的行政单位,我们称之为 barangays。所提出的方法解决了一个设施选址问题产生的最小化问题,该问题是基于接种点与 barangays 的接近程度、COVID-19 病例数量以及 barangays 的人口密度来制定的。这些目标被制定为一个单一的优化问题。作为另一种决策支持工具,我们开发了一个考虑距离和人口覆盖范围的双目标优化问题。最后,我们提出了一种动态优化方法,该方法重新计算最佳接种点,以考虑已完成接种计划的 barangays 人口变化。提出了一种用于解决优化问题的数值方案,并将算法的详细描述(用 Python 和 MATLAB 编写)上传到公共存储库。作为说明,我们将我们的方法应用于确定菲律宾八打雁省圣胡安市疫苗接种点的最佳位置。我们希望这项研究可以为地方政府机构制定战略和可及的疫苗接种计划提供指导。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ede/9528907/8bc8affa1cb5/peerj-10-14151-g001.jpg

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