Emergency Medical Resources Allocation of Periphery for Epidemic Areas: Based on Infectious Diseases Spatial-Temporal Transmission Path
People in the epicenter suffer from emergency medical supplies shortage in the early stage of a public health emergency because of imbalanced supply-demand in different regions or areas, which is a key issue in a major infectious disease. In response to the severe insufficiency of supplies in the ep...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2023/8841451 |
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| _version_ | 1832559320285315072 |
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| author | Xuan Zhao Benhong Peng Chaoyu Zheng Anxia Wan |
| author_facet | Xuan Zhao Benhong Peng Chaoyu Zheng Anxia Wan |
| author_sort | Xuan Zhao |
| collection | DOAJ |
| description | People in the epicenter suffer from emergency medical supplies shortage in the early stage of a public health emergency because of imbalanced supply-demand in different regions or areas, which is a key issue in a major infectious disease. In response to the severe insufficiency of supplies in the epicenter, this study proposed a strategy of distributing supplies from peripheral areas to the epicenter and gave a supply-side selection model considering the epidemic influence and supplies condition in the candidate supply-side areas. First of all, the epidemic spatial-temporal transmission path (STTP) network describing the geographic spread of disease is obtained using a first-order conditional dependence approximation algorithm in a dynamic Bayesian network (DBN). Then, the structural information of the STTP network and the supplies condition characteristic information are combined using the Bipartite network embedding (BiNE) method. Finally, a graph convolutional neural network (GCN) is conducted to select the supply-side areas for peripheral-epicenter supplies distribution based on information achieved from the bipartite graph. The results show that the highest supplies allocation accuracy reaches 87%. Validation and supremacy of the proposed methodology are provided by applying it to the case in Hubei province. This study considers crossed-areas supplies distribution strategy and contributes to select suitable supply-side areas considering the epidemic and supplies condition in the peripheral areas, which is helpful to both epicenter and peripheral areas. |
| format | Article |
| id | doaj-art-f6461f04046e44088ba46cba47a6d7c3 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-f6461f04046e44088ba46cba47a6d7c32025-02-03T01:30:25ZengWileyComplexity1099-05262023-01-01202310.1155/2023/8841451Emergency Medical Resources Allocation of Periphery for Epidemic Areas: Based on Infectious Diseases Spatial-Temporal Transmission PathXuan Zhao0Benhong Peng1Chaoyu Zheng2Anxia Wan3School of Management Science and EngineeringSchool of Management Science and EngineeringSchool of Management Science and EngineeringSchool of Management Science and EngineeringPeople in the epicenter suffer from emergency medical supplies shortage in the early stage of a public health emergency because of imbalanced supply-demand in different regions or areas, which is a key issue in a major infectious disease. In response to the severe insufficiency of supplies in the epicenter, this study proposed a strategy of distributing supplies from peripheral areas to the epicenter and gave a supply-side selection model considering the epidemic influence and supplies condition in the candidate supply-side areas. First of all, the epidemic spatial-temporal transmission path (STTP) network describing the geographic spread of disease is obtained using a first-order conditional dependence approximation algorithm in a dynamic Bayesian network (DBN). Then, the structural information of the STTP network and the supplies condition characteristic information are combined using the Bipartite network embedding (BiNE) method. Finally, a graph convolutional neural network (GCN) is conducted to select the supply-side areas for peripheral-epicenter supplies distribution based on information achieved from the bipartite graph. The results show that the highest supplies allocation accuracy reaches 87%. Validation and supremacy of the proposed methodology are provided by applying it to the case in Hubei province. This study considers crossed-areas supplies distribution strategy and contributes to select suitable supply-side areas considering the epidemic and supplies condition in the peripheral areas, which is helpful to both epicenter and peripheral areas.http://dx.doi.org/10.1155/2023/8841451 |
| spellingShingle | Xuan Zhao Benhong Peng Chaoyu Zheng Anxia Wan Emergency Medical Resources Allocation of Periphery for Epidemic Areas: Based on Infectious Diseases Spatial-Temporal Transmission Path Complexity |
| title | Emergency Medical Resources Allocation of Periphery for Epidemic Areas: Based on Infectious Diseases Spatial-Temporal Transmission Path |
| title_full | Emergency Medical Resources Allocation of Periphery for Epidemic Areas: Based on Infectious Diseases Spatial-Temporal Transmission Path |
| title_fullStr | Emergency Medical Resources Allocation of Periphery for Epidemic Areas: Based on Infectious Diseases Spatial-Temporal Transmission Path |
| title_full_unstemmed | Emergency Medical Resources Allocation of Periphery for Epidemic Areas: Based on Infectious Diseases Spatial-Temporal Transmission Path |
| title_short | Emergency Medical Resources Allocation of Periphery for Epidemic Areas: Based on Infectious Diseases Spatial-Temporal Transmission Path |
| title_sort | emergency medical resources allocation of periphery for epidemic areas based on infectious diseases spatial temporal transmission path |
| url | http://dx.doi.org/10.1155/2023/8841451 |
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