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			<depositor_name>Clinical Intelligence Research Press</depositor_name>
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				<full_title>Journal of Artificial Intelligence for Healthcare Systems</full_title>
				<abbrev_title>J. Artif. Intell. Healthc. Syst.</abbrev_title>
				<issn>3149-8981</issn>
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					<year>2026</year>
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					<volume>5</volume>
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				<issue>2</issue>
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					<title>Predictive Analytics for Healthcare Supply Chain Resilience during Public Health Emergencies: A Systematic Review of Models for Personal Protective Equipment Demand Forecasting and Distribution Optimization</title>
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          					<person_name sequence="first" contributor_role="author">
            <given_name>Anna</given_name>
            <surname>Novak</surname>
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            <given_name>Tomas</given_name>
            <surname>Hruby</surname>
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					<year>2026</year>
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					  <unstructured_citation>Cohen J, van der Meulen Rodgers Y. Contributing factors to personal protective equipment shortages during the COVID-19 pandemic. Prev Med. 2020;141:106263.</unstructured_citation>
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					  <unstructured_citation>Lam SC. Sourcing personal protective equipment during the COVID-19 pandemic: challenging the principle of “reasonably practicable” by the flooding of counterfeit and fake face masks during the COVID-19 pandemic. JAMA. 2020;323(16):1601-2.</unstructured_citation>
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					  <unstructured_citation>Finkenstadt DJ, Handfield R. Blurry vision: supply chain visibility for personal protective equipment during COVID-19. J Purch Supply Manag. 2021;27(3):100689.</unstructured_citation>
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					  <unstructured_citation>Gereffi G. What does the COVID-19 pandemic teach us about global value chains? The case of medical supplies. J Int Bus Policy. 2020;3(3):287-301.</unstructured_citation>
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					  <unstructured_citation>Singh SK, Khawale RP, Chen H, Zhang H, Rai R. Personal protective equipments (PPEs) for COVID-19: a product lifecycle perspective. Int J Prod Res. 2022;60(10):3282-303.</unstructured_citation>
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					  <unstructured_citation>Arji G, Ahmadi H, Avazpoor P, Hemmat M. Identifying resilience strategies for disruption management in the healthcare supply chain during COVID-19 by digital innovations: a systematic literature review. Inform Med Unlocked. 2023;38:101199.</unstructured_citation>
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					  <unstructured_citation>Longo F, Mirabelli G, Padovano A, Solina V. The Digital Supply Chain Twin paradigm for enhancing resilience and sustainability against COVID-like crises. Procedia Comput Sci. 2023;217:1940-7.</unstructured_citation>
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          					<citation key="rk-10.68159/c533679438-63626608-c4e2-4685-bd52-6714257771e0">
					  <unstructured_citation>Burgos D, Ivanov D. Food retail supply chain resilience and the COVID-19 pandemic: a digital twin-based impact analysis and improvement directions. Transp Res E Logist Transp Rev. 2021;152:102412.</unstructured_citation>
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					  <unstructured_citation>Ivanov D, Dolgui A. A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Prod Plan Control. 2021;32(9):775-88.</unstructured_citation>
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					  <unstructured_citation>Cimino A, Longo F, Mirabelli G, Solina V. A cyclic and holistic methodology to exploit the Supply Chain Digital Twin concept towards a more resilient and sustainable future. Clean Logist Supply Chain. 2024;11:100154.</unstructured_citation>
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					  <unstructured_citation>Zhao Y, Zhang C, Jing S, Chang X, Zhao C. Location-routing optimization research of personal protective equipment considering priority under supply and demand uncertainty. J Oper Res Soc. 2025;76(9):1962-80.</unstructured_citation>
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					  <unstructured_citation>Zong K, Luo C. Reinforcement learning based framework for COVID-19 resource allocation. Comput Ind Eng. 2022;167:107960.</unstructured_citation>
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