A global blockchain for recording high rates of COVID-19 vaccinations

Document Type

Article

Publication Date

9-1-2023

Abstract

Blockchain has been recently proposed to securely record vaccinations against COVID-19 and manage their verification. However, existing solutions may not fully meet the requirements of a global vaccination management system. These requirements include the scalability required to support a global vaccination campaign, like one against COVID-19, and the capability to facilitate the interoperation between the independent health administrations of different countries. Moreover, access to global statistics can help to control securing community health and provide continuity of care for individuals during a pandemic. In this paper, we propose GEOS, a blockchain-based vaccination management system designed to address the challenges faced by the global vaccination campaign against COVID-19. GEOS offers interoperability between vaccination information systems at both domestic and international levels, supporting high vaccination rates and extensive coverage for the global population. To provide those features, GEOS uses a two-layer blockchain architecture, a simplified byzantine-tolerant consensus algorithm, and the Boneh–Lynn–Shacham signature scheme. We analyze the scalability of GEOS by examining transaction rate and confirmation times, considering factors such as the number of validators, communication overhead, and block size within the blockchain network. Our findings demonstrate the effectiveness of GEOS in managing COVID-19 vaccination records and statistical data for 236 countries, encompassing crucial information such as daily vaccination rates for highly populous nations and the global vaccination demand, as identified by the World Health Organization.

Identifier

85161689033 (Scopus)

Publication Title

Computers in Biology and Medicine

External Full Text Location

https://doi.org/10.1016/j.compbiomed.2023.107074

e-ISSN

18790534

ISSN

00104825

PubMed ID

37311384

Volume

163

Fund Ref

New York Institute of Technology

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