A data-driven spatially-specific vaccine allocation framework for COVID-19.
Ann Oper Res
; : 1-24, 2022 Nov 22.
Article
in English
| MEDLINE | ID: covidwho-2128778
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Prognostic study
Topics:
Vaccines
Language:
English
Journal:
Ann Oper Res
Year:
2022
Document Type:
Article
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