基于智能复杂网络的突发传染病模型与决策支持研究
结题报告
批准号:
72004174
项目类别:
青年科学基金项目
资助金额:
24.0 万元
负责人:
李冰
依托单位:
学科分类:
公共安全与应急管理
结题年份:
2023
批准年份:
2020
项目状态:
已结题
项目参与者:
李冰
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中文摘要
基于复杂网络建立突发传染病模型,实现传染规模、重点区域的预测;以模型为基础,计算防控工作所需的人力、物资,模拟防控措施干预下的传染病发展过程,从而为突发传染病提供决策支持。以计算机技术提升传染病防控工作的精准度和有效性,推进我国公共卫生事业发展。. 为了实现这个目标,完成了以下工作:(1)根据传染病管理流程,以区块链技术建立数据库,提高管理效率和降低时间成本;(2)结合自然语言处理和深度计算机视觉实现相似病例提取和概率性的预诊断,突破异源上报对新型传染病发现的限制,完成传染模型患病节点的标记;(3)特征选择方法挖掘社区扩散规则,结合标签传播理论构建规则指引的社区发现算法,以患病节点为基准,识别潜在和密切接触者;(4)防控措施干预的传染病模型衍化和多元线性回归的防控资源估算共同作用下的分级式预警机制,为突发传染病的快速响应提供决策支持。
英文摘要
A sudden epidemic model based on complex network is proposed to find the tendency and important region. The necessary resources are estimated to control the addition of infectious nodes showing by the model; moreover, the impact variation under intervention is simulated by the model. Then, all the results are employed to make decision support. The main idea of this project is to improve the accuracy and effectiveness of preventing and controlling infectious disease by computer technology, which is helpful for development of public health in our country. ..In order to get the aims, these works described in the below are done. (1) Under the process of management, a data set of infectious disease is built using the theory of block chain to improve the efficiency of management and cut time cost. (2) Natural language processing and computer vision provided by deep learning are both used to discriminate infectious cases and give stochastic diagnosis, which can quickly break the limitation taken by region. Then, nodes in the model are labeled. (3) Feature selection can form the infectious rules by mining the relationship among the data. Label propagation coached by the rules detects the network in the model to search uncertain nodes and close contact. (4) Based on both the estimation of necessary resource using multiple linear regression and the result of preventing and controlling, grading warning advanced the response to sudden outbreak of infectious disease.
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DOI:--
发表时间:2022
期刊:武 汉 理 工 大 学 学 报 ( 信 息 与 管 理 工 程 版 )
影响因子:--
作者:陈冬林;马一帆;李冰
通讯作者:李冰
DOI:10.1007/s00521-022-07448-2
发表时间:2022-06
期刊:Neural Computing and Applications
影响因子:6
作者:Choujun Zhan;Wei Jiang;Fabing Lin;Shuntao Zhang;Bing Li
通讯作者:Choujun Zhan;Wei Jiang;Fabing Lin;Shuntao Zhang;Bing Li
DOI:10.1007/s11009-022-09941-6
发表时间:2022-02
期刊:Methodology and Computing in Applied Probability
影响因子:0.9
作者:Fenge Chen;Bing Li;Xingchun Peng
通讯作者:Fenge Chen;Bing Li;Xingchun Peng
DOI:10.3390/math11122697
发表时间:2023-06-01
期刊:MATHEMATICS
影响因子:2.4
作者:Li,Bing;Xiang,Ziye
通讯作者:Xiang,Ziye
DOI:10.3390/electronics12204337
发表时间:2023-10-01
期刊:ELECTRONICS
影响因子:2.9
作者:Li,Bing;Liu,Qi
通讯作者:Liu,Qi
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海外基金