{"title":"A guided twin delayed deep deterministic reinforcement learning for vaccine allocation in human contact networks","authors":"","doi":"10.1016/j.asoc.2024.112322","DOIUrl":null,"url":null,"abstract":"<div><div>This manuscript introduces an innovative approach to optimizing the distribution of a limited vaccine resource within a population modeled as a contact network, aiming to mitigate the spread of infectious diseases. The study develops a novel methodology that combines reinforcement learning and graph neural networks. To understand the dynamics of disease propagation, the study constructs an analytical model that outlines conditions for disease eradication or endemic states. This model supports a series of simulation experiments across various scenarios, demonstrating the proposed method’s superiority over random and centrality-based approaches in reducing the average number of infections per individual during an outbreak. The adaptability of the proposed method is further emphasized by its robust performance across networks of diverse sizes and configurations, highlighting its real-world applicability. The findings of this study have significant implications for public health policy and resource allocation, offering a promising framework for managing infectious disease outbreaks in complex and dynamic environments.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":null,"pages":null},"PeriodicalIF":7.2000,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Soft Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1568494624010962","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
This manuscript introduces an innovative approach to optimizing the distribution of a limited vaccine resource within a population modeled as a contact network, aiming to mitigate the spread of infectious diseases. The study develops a novel methodology that combines reinforcement learning and graph neural networks. To understand the dynamics of disease propagation, the study constructs an analytical model that outlines conditions for disease eradication or endemic states. This model supports a series of simulation experiments across various scenarios, demonstrating the proposed method’s superiority over random and centrality-based approaches in reducing the average number of infections per individual during an outbreak. The adaptability of the proposed method is further emphasized by its robust performance across networks of diverse sizes and configurations, highlighting its real-world applicability. The findings of this study have significant implications for public health policy and resource allocation, offering a promising framework for managing infectious disease outbreaks in complex and dynamic environments.
期刊介绍:
Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities.
Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.