Industry 4.0 Technologies for the Manufacturing and Distribution of COVID-19 Vaccines.
Background: The evolutionary stages of manufacturing have led us to conceptualize the use of Industry 4.0 for COVID-19 (coronavirus disease 2019), powered by Industry 4.0 technologies. Using applications of integrated process optimizations reliant on digitized data, we propose novel intelligent netw...
| Publicado en: | Journal of Primary Care & Community Health Vol. 13; pp. 1 - 6 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Jan-Dec2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=161704135&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161704135 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 21501319 AZBE jtl: Journal of Primary Care & Community Health issn: 21501319 maglogo: Y pubinfo: dt: Jan-Dec2022 vid: 13 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 161704135 161704135 161704135 10.1177/21501319211068638 161704135 ppf: 1 ppct: 5 formats: tig: atl: Industry 4.0 Technologies for the Manufacturing and Distribution of COVID-19 Vaccines. aug: au: Sarfraz, Azza Sarfraz, Zouina Sarfraz, Muzna Razzack, Aminah Abdul Bano, Shehar Makkar, Sarabjot Singh Thevuthasan, Sindhu Paul, Trissa Sana, Muhammad Khawar Azeem, Nishwa Felix, Miguel Cherrez-Ojeda, Ivan affil: Larkin Community Hospital, South Miami, FL, USA sug: subj: Artificial Intelligence COVID-19 Vaccines Supply and Distribution COVID-19 Prevention and Control Digital Technology Biosensing Techniques Industry Health Services Accessibility Developing Countries Digitizers Low and Middle Income Countries Computer-Aided Design Biometrics Conceptual Framework Biosensors Blockchain Public Health ab: Background: The evolutionary stages of manufacturing have led us to conceptualize the use of Industry 4.0 for COVID-19 (coronavirus disease 2019), powered by Industry 4.0 technologies. Using applications of integrated process optimizations reliant on digitized data, we propose novel intelligent networks along the vaccine value chain. Vaccine 4.0 may enable maintenance processes, streamline logistics, and enable optimal production of COVID-19 vaccines. Vaccine 4.0 Framework: The challenge in applying Vaccine 4.0 includes the requirement of large-scale technologies for digitally transforming manufacturing, producing, rolling-out, and distributing vaccines. With our framework, Vaccine 4.0 analytics will target process performance, process development, process stability, compliance, quality assessment, and optimized maintenance. The benefits of digitization during and post the COVID-19 pandemic include first, the continual assurance of process control, and second, the efficacy of big-data analytics in streamlining set parameter limits. Digitization including big data-analytics may potentially improve the quality of large-scale vaccine production, profitability, and manufacturing processes. The path to Vaccine 4.0 will enhance vaccine quality, improve efficacy, and compliance with data-regulated requirements. Discussion: Fiscal and logistical barriers are prevalent across resource-limited countries worldwide. The Vaccine 4.0 framework accounts for expected barriers of manufacturing and equitably distributing COVID-19 vaccines. With amalgamating big data analytics and biometrics, we enable the identification of vulnerable populations who are at higher risk of disease transmission. Artificial intelligence powered sensors and robotics support thermostable vaccine distribution in limited capacity regions, globally. Biosensors isolate COVID-19 vaccinations with low or limited efficacy. Finally, Vaccine 4.0 blockchain systems address low- and middle-income countries with limited distribution capacities. Conclusion: Vaccine 4.0 is a viable framework to optimize manufacturing of vaccines during and post the COVID-19 pandemic. pubtype: Academic Journal doctype: tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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