Modelling and disrupting counterfeit N95 respirator supply chains
Publication type: Journal Article
Publication date: 2025-03-01
scimago Q1
wos Q1
SJR: 1.993
CiteScore: 13.1
Impact factor: 9.9
ISSN: 14740346, 18735320
Abstract
During the COVID-19 pandemic, millions of counterfeit respirators and fraudulent medical products infiltrated legitimate supply chains, often facilitated by registered businesses, third-party logistics providers, and companies in the technology sector. The trade in counterfeit respirators during the global health crisis threatened public health, safety, and security. The study uses seizure data, as well as analysis of shipping records and investigation reports to understand illicit supply chains of counterfeit N95 respirators. To compare the effectiveness of different types of disruption strategies, the authors propose a multi-period optimization problem and study different types of disruption strategies that would undermine counterfeit respirator supply chains. The authors also share numerical experiments and findings concerning the effectiveness of the proposed model.
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Advanced Engineering Informatics
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Total citations:
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Citations from 2024:
1
(100%)
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Huang E. et al. Modelling and disrupting counterfeit N95 respirator supply chains // Advanced Engineering Informatics. 2025. Vol. 64. p. 103084.
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Huang E., Shelley L. Modelling and disrupting counterfeit N95 respirator supply chains // Advanced Engineering Informatics. 2025. Vol. 64. p. 103084.
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TY - JOUR
DO - 10.1016/j.aei.2024.103084
UR - https://linkinghub.elsevier.com/retrieve/pii/S1474034624007353
TI - Modelling and disrupting counterfeit N95 respirator supply chains
T2 - Advanced Engineering Informatics
AU - Huang, Edward
AU - Shelley, Louise
PY - 2025
DA - 2025/03/01
PB - Elsevier
SP - 103084
VL - 64
SN - 1474-0346
SN - 1873-5320
ER -
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BibTex (up to 50 authors)
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@article{2025_Huang,
author = {Edward Huang and Louise Shelley},
title = {Modelling and disrupting counterfeit N95 respirator supply chains},
journal = {Advanced Engineering Informatics},
year = {2025},
volume = {64},
publisher = {Elsevier},
month = {mar},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1474034624007353},
pages = {103084},
doi = {10.1016/j.aei.2024.103084}
}