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Usage Information

SARS-CoV-2 vaccines: a triumph of science and collaboration
Jonathan L. Golob, Njira Lugogo, Adam S. Lauring, Anna S. Lok
Jonathan L. Golob, Njira Lugogo, Adam S. Lauring, Anna S. Lok
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Review

SARS-CoV-2 vaccines: a triumph of science and collaboration

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Abstract

Roughly 1 year after the first case of COVID-19 was identified and less than 1 year after the sequencing of SARS-CoV-2, multiple SARS-CoV-2 vaccines with demonstrated safety and efficacy in phase III clinical trials are available. The most promising vaccines have targeted the surface glycoprotein (S-protein) of SARS-CoV-2 and achieved an approximate 85%–95% reduction in the risk of symptomatic COVID-19, while retaining excellent safety profiles and modest side effects in the phase III clinical trials. The mRNA, replication-incompetent viral vector, and protein subunit vaccine technologies have all been successfully employed. Some novel SARS-CoV-2 variants evade but do not appear to fully overcome the potent immunity induced by these vaccines. Emerging real-world effectiveness data add evidence for protection from severe COVID-19. This is an impressive first demonstration of the effectiveness of the mRNA vaccine and vector vaccine platforms. The success of SARS-CoV-2 vaccine development should be credited to open science, industry partnerships, harmonization of clinical trials, and the altruism of study participants. The manufacturing and distribution of the emergency use–authorized SARS-CoV-2 vaccines are ongoing challenges. What remains now is to ensure broad and equitable global vaccination against COVID-19.

Authors

Jonathan L. Golob, Njira Lugogo, Adam S. Lauring, Anna S. Lok

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Usage data is cumulative from December 2024 through December 2025.

Usage JCI PMC
Text version 1,457 288
PDF 201 48
Figure 393 7
Table 63 0
Citation downloads 172 0
Totals 2,286 343
Total Views 2,629
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Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.

Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.

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