Emergency Medical Resources Allocation of Periphery for Epidemic Areas: Based on Infectious Diseases Spatial-Temporal Transmission Path

Xuan Zhao, Benhong Peng, Chaoyu Zheng, Anxia Wan and Chittaranjan Hens

Complexity, 2023, vol. 2023, 1-13

Abstract: 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.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:8841451

DOI: 10.1155/2023/8841451

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