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In the post-pandemic era, there should be a greater emphasis on enhancing future protective capabilities, particularly in the rational allocation of medical resources. During the pandemic, the stability of the healthcare system enabled effective control of the situation, yet there remain significant shortcomings in resource distribution that need to be addressed. Specifically, issues related to the route planning of mobile testing stations, the number of confirmed cases, and the time series have often been insufficiently prioritized in the past. Therefore, this study employs a clustering analysis strategy to not only flexibly arrange the number of mobile testing stations but also effectively reduce the number of relocations. The results indicate that this approach can significantly optimize the allocation of medical resources in cities with a larger number of administrative divisions, greatly decreasing the numbers of movements across different time periods and further promoting the sustainable development of medical resources.

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Clustering Analysis and Time Series Approaches for Effective Resource Allocation and Route Planning in Managing Confirmed Cases

  • 会议论文

  • 发表日期：

    2025年

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  • You-Tung Chen,
  • Hsieh-Chih Hsu,
  • Shih-Hsiung Lee,
  • Chu-Sing Yang,
  • Chien-Chih Chen,
  • Pang-Wei Tsai,
  • Ko-Wei Huang

摘要

In the post-pandemic era, there should be a greater emphasis on enhancing future protective capabilities, particularly in the rational allocation of medical resources. During the pandemic, the stability of the healthcare system enabled effective control of the situation, yet there remain significant shortcomings in resource distribution that need to be addressed. Specifically, issues related to the route planning of mobile testing stations, the number of confirmed cases, and the time series have often been insufficiently prioritized in the past. Therefore, this study employs a clustering analysis strategy to not only flexibly arrange the number of mobile testing stations but also effectively reduce the number of relocations. The results indicate that this approach can significantly optimize the allocation of medical resources in cities with a larger number of administrative divisions, greatly decreasing the numbers of movements across different time periods and further promoting the sustainable development of medical resources.