ArticleFrontiers in research metrics and analytics2023
The research foundation for COVID-19 vaccine development.
Article in Frontiers in research metrics and analytics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed, 5 citations in OpenAlex.
- Computational tools and data integration to accelerate vaccine development: challenges, opportunities, and future directions.Frontiers in immunology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of effective vaccines in <1 year to combat the spread of coronavirus disease 19 (COVID-19) is an example of particularly rapid progress in biomedicine. However, this was only made possible by decades of investment in scientific research. Many important research commentaries and reviews have been provided to describe the various contributions and scientific breakthroughs that led to the development of COVID-19 vaccines. In this work, we sought to complement those efforts by adding a systematic and quantitative study of the research foundations that led to these vaccines. Here, we analyzed citations from COVID-19 vaccine research articles to determine which scientific areas of study contributed the most to this research. Our findings revealed that coronavirus research was cited most often, and by a large margin. However, significant contributions were also seen from a diverse set of fields such as cancer, diabetes, and HIV/AIDS. In addition, we examined the publication history of the most prolific authors of COVID-19 vaccine research to determine their research expertise prior to the pandemic. Interestingly, although COVID-19 vaccine research relied most heavily on previous coronavirus work, we find that the most prolific authors on these publications most often had expertise in other areas including influenza, cancer, and HIV/AIDS. Finally, we used machine learning to identify and group together publications based on their major topic areas. This allowed us to elucidate the differences in citations between research areas. These findings highlight and quantify the relevance of prior research from a variety of scientific fields to the rapid development of a COVID-19 vaccine. This study also illustrates the importance of funding and sustaining a diverse research enterprise to facilitate a rapid response to future pandemics.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.