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

Zhaofu Hong, Yingjie Li, Yeming Gong () and Wanying Amanda Chen
Additional contact information
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]

Post-Print from HAL

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.

Keywords: Vaccine allocation; Data-driven decision making; COVID-19; Spatially-specific SEIR model; Deep learning (search for similar items in EconPapers)
Date: 2024-08-01
References: Add references at CitEc
Citations:

Published in Annals of Operations Research, 2024, 339 (1-2), pp.203 - 226. ⟨10.1007/s10479-022-05037-z⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05635790

DOI: 10.1007/s10479-022-05037-z

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().