학술논문

Hierarchical Vaccine Allocation Based on Epidemiological and Behavioral Considerations
Document Type
Periodical
Source
IEEE/ACM Transactions on Computational Biology and Bioinformatics IEEE/ACM Trans. Comput. Biol. and Bioinf. Computational Biology and Bioinformatics, IEEE/ACM Transactions on. 20(5):2981-2991 Jan, 2023
Subject
Bioengineering
Computing and Processing
Vaccines
Resource management
Statistics
Sociology
Optimization
Costs
COVID-19
optimization
public policymaking
vaccines allocation
Language
ISSN
1545-5963
1557-9964
2374-0043
Abstract
Vaccines have proven useful in curbing contagion from new strains of the SARS-CoV-2 virus. However, equitable vaccine allocation continues to be a significant challenge worldwide, necessitating a comprehensive allocation strategy incorporating heterogeneity in epidemiological and behavioral considerations. In this paper, we present a hierarchical allocation strategy that assigns vaccines to zones and their constituent neighborhoods cost-effectively, based on their population density, susceptibility, infected count, and attitude towards vaccinations. Moreover, it includes a module that tackles vaccine shortages in certain zones by locally transferring vaccines from zones with surplus vaccines. We leverage the epidemiological, socio-demographic, and social media datasets from Chicago and Greece and their constituent community areas to show that the proposed allocation approach assigns vaccines based on the chosen criteria and captures the effects of disparate vaccine adoption rates. We conclude the paper with a lowdown on future efforts to extend this study to design models for effective public policies and vaccination strategies that curtail vaccine purchase costs.