Alghanmi Nusaybah, Alotaibi Reem, Alshammari Sultanah, Mahmood Arif
Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
Sci Rep. 2025 Jul 28;15(1):27491. doi: 10.1038/s41598-025-12898-4.
Public health emergencies, such as disease outbreaks, require health authorities to set up points of dispensing (PODs) to efficiently distribute vaccines and drugs to attempt to reduce infection spread within a short timeframe. A considerable amount of research has been conducted to determine the number and location of new PODs while keeping existing ones operational. A query-based approach needs data on the number of new PODs in advance, and an optimisation-based approach requires the specification of distance and percentage gap as constraints or objective to ensure the required population coverage is achieved. The model presented here overcomes such issues. It takes an influence-based approach for POD location-allocation (IPLA) based on a new Influence Affinity Propagation (IAP) clustering algorithm to designate the number and location of new PODs where PODs already exist. IAP takes the influence of existing PODs as a novel weight, that is, the total population size attracted by existing PODs with minimum distance, in addition to geographical information. The proposed model is applied to three synthetic datasets and a real case, namely: the COVID-19 pandemic in Jeddah, Saudi Arabia. IPLA demonstrates better population coverage with a lower number of new PODs than other approaches. It also obtained the closest results to the case study in terms of number, location and predictive performance.
公共卫生突发事件，如疾病爆发，要求卫生当局设立配药点（PODs），以便高效分发疫苗和药物，试图在短时间内减少感染传播。已经开展了大量研究来确定新配药点的数量和位置，同时维持现有配药点的运营。基于查询的方法需要提前获取新配药点数量的数据，而基于优化的方法则需要指定距离和百分比差距作为约束条件或目标，以确保实现所需的人口覆盖范围。本文提出的模型克服了这些问题。它基于一种新的影响亲和传播（IAP）聚类算法，采用基于影响的配药点位置分配方法（IPLA）来确定在已有配药点的情况下新配药点的数量和位置。IAP除了考虑地理信息外，还将现有配药点的影响作为一种新的权重，即受现有配药点吸引且距离最短的总人口规模。所提出的模型应用于三个合成数据集和一个实际案例，即沙特阿拉伯吉达的新冠肺炎疫情。与其他方法相比，IPLA以更少的新配药点实现了更好的人口覆盖。在数量、位置和预测性能方面，它也获得了与案例研究最接近的结果。
