Evidence map›Paper›PMID 34984932›Full record

ArticleJournal of primary care & community health

Industry 4.0 Technologies for the Manufacturing and Distribution of COVID-19 Vaccines.

Azza Sarfraz, Zouina Sarfraz, Muzna Sarfraz, Aminah Abdul Razzack, Shehar Bano, Sarabjot Singh Makkar, Sindhu Thevuthasan, Trissa Paul, Muhammad Khawar Sana, Nishwa Azeem and 2 more

Abstract read
In one paragraph

Article in Journal of primary care & community health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Azza SarfrazLarkin Community Hospital, South Miami, FL, USA.ORCID https://orcid.org/0000-0001-8206-5745
Zouina SarfrazLarkin Community Hospital, South Miami, FL, USA.ORCID https://orcid.org/0000-0002-5132-7455
Muzna SarfrazLarkin Community Hospital, South Miami, FL, USA.
Aminah Abdul RazzackLarkin Community Hospital, South Miami, FL, USA.
Shehar BanoFatima Jinnah Medical University, Lahore, Pakistan.ORCID https://orcid.org/0000-0002-6429-797X
Sarabjot Singh MakkarLarkin Community Hospital, South Miami, FL, USA.
Sindhu ThevuthasanLarkin Community Hospital, South Miami, FL, USA.
Trissa PaulLarkin Community Hospital, South Miami, FL, USA.
Muhammad Khawar SanaLarkin Community Hospital, South Miami, FL, USA.
Nishwa AzeemLahore General Hospital, Lahore, Pakistan.
Miguel FelixUniversidad Espiritu Santo, Samborondon, Ecuador.
Ivan Cherrez-OjedaUniversidad Espiritu Santo, Samborondon, Ecuador.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

conclusionVaccine 4.0 is a viable framework to optimize manufacturing of vaccines during and post the COVID-19 pandemic.

Indexed as

COVID-19COVID-19 VaccinesArtificial IntelligenceHumansPandemicsSARS-CoV-2COVID-19 Vaccinescommunity healthCOVID-19global healthpolicypopulationsvaccination

Identifiers

PMID34984932
PMCPMC8753236

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.