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

Zhaofu Hong1, Yingjie Li2,3, Yeming Gong4

  • 1School of Management, Northwestern Polytechnical University, Xi'an, People's Republic of China.

Annals of Operations Research
|December 5, 2022
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Summary

This study introduces a data-driven framework for allocating coronavirus disease 2019 (COVID-19) vaccines to minimize deaths and infections. The optimal strategy involves prioritizing the outbreak

Keywords:
COVID-19Data-driven decision makingDeep learningSpatially-specific SEIR modelVaccine allocation

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Area of Science:

  • Epidemiology
  • Public Health
  • Data Science

Background:

  • Coronavirus disease 2019 (COVID-19) vaccine allocation presents significant challenges.
  • Effective distribution strategies are crucial for mitigating disease spread and mortality.
  • Existing allocation policies may not fully account for spatial variations in risk.

Purpose of the Study:

  • To develop and evaluate a data-driven, spatially-specific framework for COVID-19 vaccine allocation.
  • To minimize COVID-19-related deaths and infections through optimized vaccine distribution.
  • To compare the proposed framework's performance against current allocation policies.

Main Methods:

  • Utilized a self-organizing map neural network for regional risk-level classification.
  • Incorporated a spatially-specific disease progression model.
  • Developed a vaccine allocation model considering production capacity and regional risk data from China (Jan-Feb 2020).

Main Results:

  • The proposed spatially-specific vaccine allocation framework aims to minimize COVID-19 deaths and infections.
  • Vaccines should be allocated first to the outbreak's source region, then to other regions based on decreasing risk.
  • This policy demonstrated superior performance compared to existing allocation strategies.

Conclusions:

  • A data-driven, spatially-specific approach is effective for COVID-19 vaccine allocation.
  • Prioritizing the source region and then distributing based on risk significantly improves outcomes.
  • The framework offers a more optimized strategy for vaccine distribution during pandemics.