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Disease X vaccine production and supply chains: risk assessing healthcare systems operating with artificial intelligence and industry 4.0

Abstract:

Objective: The objective of this theoretical paper is to identify conceptual solutions for securing, predicting, and improving vaccine production and supply chains.

Method: The case study, action research, and review method is used with secondary data – publicly available open access data.

Results: A set of six algorithmic solutions is presented for resolving vaccine production and supply chain bottlenecks. A different set of algorithmic solutions is presented for forecasting risks during a Disease X event. A new conceptual framework is designed to integrate the emerging solutions in vaccine production and supply chains. The framework is constructed to improve the state-of-the-art by intersecting the previously isolated disciplines of edge computing; cyber-risk analytics; healthcare systems, and AI algorithms.

Conclusion: For healthcare systems to cope better during a disease X event than during Covid-19, we need multiple highly specific AI algorithms, targeted for solving specific problems. The proposed framework would reduce production and supply chain risk and complexity in a Disease X event.

Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/s12553-022-00722-2

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0001-5629-6857
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Oxford e-Research Centre
Role:
Author
ORCID:
0000-0001-9074-3016


Publisher:
Springer
Journal:
Health and Technology More from this journal
Volume:
13
Issue:
1
Pages:
11–15
Publication date:
2023-01-04
Acceptance date:
2022-12-21
DOI:
EISSN:
2190-7196
ISSN:
2190-7188


Language:
English
Keywords:
Pubs id:
1317568
Local pid:
pubs:1317568
Deposit date:
2023-01-02

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