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A data-driven spatially-specific vaccine allocation framework for COVID-19

Author

Listed:
  • Zhaofu Hong

    (NPU - Northernwest Polytechnical University [Xi'an])

  • Yingjie Li

    (Central South University (China, Changsha) - CSU)

  • Yeming Gong

    (EM - EMLyon Business School)

  • Wanying Amanda Chen

    (ZJSU - Zhejiang Gongshang University [Hangzhou])

Abstract

Although coronavirus disease 2019 (COVID-19) vaccines have been introduced, their allocation is a challenging problem. We propose a data-driven, spatially-specific vaccine allocation framework that aims to minimize the number of COVID-19-related deaths or infections. The framework combines a regional risk-level classification model solved by a self-organizing map neural network, a spatially-specific disease progression model, and a vaccine allocation model that considers vaccine production capacity. We use data obtained from Wuhan and 35 other cities in China from January 26 to February 11, 2020, to avoid the effects of intervention. Our results suggest that, in region-wise distribution of vaccines, they should be allocated first to the source region of the outbreak and then to the other regions in order of decreasing risk whether the outcome measure is the number of deaths or infections. This spatially-specific vaccine allocation policy significantly outperforms some current allocation policies.

Suggested Citation

  • Zhaofu Hong & Yingjie Li & Yeming Gong & Wanying Amanda Chen, 2024. "A data-driven spatially-specific vaccine allocation framework for COVID-19," Post-Print hal-05635790, HAL.
    • Handle: RePEc:hal:journl:hal-05635790
    DOI: 10.1007/s10479-022-05037-z
    as

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