基于人群接触网络的应急医疗资源分配优化算法模型及运行仿真数据

该数据集主要面向突发公共卫生事件下的应急医疗资源分配优化算法研究，旨在为不同传染病的早期阻断提供多种资源分配方案并评估其效果。数据集通过模拟新冠、流感、猴痘三种传染病在人群接触网络上的传播动态生成。具体内容包括在六种不同资源分配策略（如最大连接资源分配策略、无拓扑资源分配策略、老年人优先资源分配策略、婴幼儿优先资源分配策略、熟人优先资源分配策略、随机资源分配策略）下的疫情传播趋势仿真数据。数据模拟的起点是先前构建的人群接触网络和哨点医院监测的疾病传播特性。通过在网络上应用不同的资源分配策略，并调整基本再生数、资源覆盖率和有效性等参数，利用传播动力学模型进行推演，最终形成了可用于对比不同干预措施效果的算法模型与运行仿真数据集。数据量约为20.4MB。

This dataset is primarily intended for research on optimal algorithms for emergency medical resource allocation during public health emergencies. It aims to provide multiple resource allocation schemes and evaluate their efficacy for the early containment of various infectious diseases. The dataset is generated by simulating the transmission dynamics of three infectious diseases—COVID-19, influenza, and monkeypox—on human contact networks. Specifically, it contains simulation data of epidemic transmission trends under six distinct resource allocation strategies: max-connection resource allocation, topology-free resource allocation, elderly-prioritized resource allocation, infant and toddler-prioritized resource allocation, acquaintance-prioritized resource allocation, and random resource allocation. The simulation starts from the previously constructed human contact network and disease transmission characteristics monitored by sentinel hospitals. By applying different resource allocation strategies on the network, adjusting parameters such as the basic reproduction number, resource coverage rate, and intervention effectiveness, and conducting simulations via transmission dynamics models, this dataset—an algorithmic model and operational simulation dataset that enables comparison of the effects of different intervention measures—is finally developed. The total data volume is approximately 20.4 MB.

搜集汇总

背景与挑战

背景概述

该数据集专注于突发公共卫生事件下的应急医疗资源分配优化算法研究，通过模拟新冠、流感、猴痘三种传染病在人群接触网络上的传播动态生成仿真数据，包含六种不同资源分配策略下的疫情传播趋势，用于评估干预措施效果。数据量约为20.4MB，由西北工业大学创建，旨在为传染病早期阻断提供多种资源分配方案。