Machine Learning-based Analysis of Publications Funded by the National Institutes of Health's Initial COVID-19 Pandemic Response

Overview

Journal Open Forum Infect Dis

Specialty Infectious Diseases

Date 2024 Apr 25

PMID 38659624

Authors

Anirudha S Chandrabhatla

Adishesh K Narahari

Taylor M Horgan

Paranjay D Patel

Jeffrey M Sturek

Claire L Davis

Patrick E H Jackson

Taison D Bell

Affiliations

Soon will be listed here.

Abstract

Background: The National Institutes of Health (NIH) mobilized more than $4 billion in extramural funding for the COVID-19 pandemic. Assessing the research output from this effort is crucial to understanding how the scientific community leveraged federal funding and responded to this public health crisis.

Methods: NIH-funded COVID-19 grants awarded between January 2020 and December 2021 were identified from NIH Research Portfolio Online Reporting Tools Expenditures and Results using the "COVID-19 Response" filter. PubMed identifications of publications under these grants were collected and the NIH tool was used to determine citation counts and focus (eg, clinical, animal). and the NIH's database were used to identify publications directly related to COVID-19. Publication titles and Medical Subject Heading terms were used as inputs to a machine learning-based model built to identify common topics/themes within the publications.

Results And Conclusions: We evaluated 2401 grants that resulted in 14 654 publications. The majority of these papers were published in peer-reviewed journals, though 483 were published to preprint servers. In total, 2764 (19%) papers were directly related to COVID-19 and generated 252 029 citations. These papers were mostly clinically focused (62%), followed by cell/molecular (32%), and animal focused (6%). Roughly 60% of preprint publications were cell/molecular-focused, compared with 26% of nonpreprint publications. The machine learning-based model identified the top 3 research topics to be clinical trials and outcomes research (8.5% of papers), coronavirus-related heart and lung damage (7.3%), and COVID-19 transmission/epidemiology (7.2%). This study provides key insights regarding how researchers leveraged federal funding to study the COVID-19 pandemic during its initial phase.

References

Tran B, Phan H, Nguyen Q, Ewedairo O, Vu G, Ha G . Pre-exposure Prophylaxis in HIV Research: A Latent Dirichlet Allocation Analysis (GAPRESEARCH). AIDS Rev. 2020; 22(2):103-111. DOI: 10.24875/AIDSRev.20000131. View

Chen Q, Allot A, Leaman R, Wei C, Aghaarabi E, Guerrerio J . LitCovid in 2022: an information resource for the COVID-19 literature. Nucleic Acids Res. 2022; 51(D1):D1512-D1518. PMC: 9825538. DOI: 10.1093/nar/gkac1005. View

Feng C, Wu Y, Gao L, Guo X, Wang Z, Xing B . Publication Landscape Analysis on Gliomas: How Much Has Been Done in the Past 25 Years?. Front Oncol. 2020; 9:1463. PMC: 6988829. DOI: 10.3389/fonc.2019.01463. View

Ntompras C, Drosatos G, Kaldoudi E . A high-resolution temporal and geospatial content analysis of Twitter posts related to the COVID-19 pandemic. J Comput Soc Sci. 2021; 5(1):687-729. PMC: 8528186. DOI: 10.1007/s42001-021-00150-8. View

Druss B, Marcus S . Tracking publication outcomes of National Institutes of Health grants. Am J Med. 2005; 118(6):658-63. DOI: 10.1016/j.amjmed.2005.02.015. View

Chen Q, Allot A, Lu Z . Keep up with the latest coronavirus research. Nature. 2020; 579(7798):193. DOI: 10.1038/d41586-020-00694-1. View

Alga A, Eriksson O, Nordberg M . Analysis of Scientific Publications During the Early Phase of the COVID-19 Pandemic: Topic Modeling Study. J Med Internet Res. 2020; 22(11):e21559. PMC: 7674137. DOI: 10.2196/21559. View

Cao G, Shen L, Evans R, Zhang Z, Bi Q, Huang W . Analysis of social media data for public emotion on the Wuhan lockdown event during the COVID-19 pandemic. Comput Methods Programs Biomed. 2021; 212:106468. PMC: 8516441. DOI: 10.1016/j.cmpb.2021.106468. View

Ha G, Phan H, Vu G, Tran B, Latkin C, Ho R . Fertility Desire in HIV/AIDS Research During 1992-2019: A Systematic Text Mining of Global Literature. AIDS Rev. 2020; 22(4):213-220. DOI: 10.24875/AIDSRev.20000026. View

10.

Li C, Liu Z, Shi R . A Bibliometric Analysis of 14,822 Researches on Myocardial Reperfusion Injury by Machine Learning. Int J Environ Res Public Health. 2021; 18(15). PMC: 8345983. DOI: 10.3390/ijerph18158231. View

11.

Chen Q, Allot A, Lu Z . LitCovid: an open database of COVID-19 literature. Nucleic Acids Res. 2020; 49(D1):D1534-D1540. PMC: 7778958. DOI: 10.1093/nar/gkaa952. View

12.

Li D, Wang Z, Wang L, Sohn S, Shen F, Murad M . A Text-Mining Framework for Supporting Systematic Reviews. Am J Inf Manag. 2017; 1(1):1-9. PMC: 5653323. View

13.

Scarpino I, Zucco C, Vallelunga R, Luzza F, Cannataro M . Investigating Topic Modeling Techniques to Extract Meaningful Insights in Italian Long COVID Narration. BioTech (Basel). 2022; 11(3). PMC: 9496775. DOI: 10.3390/biotech11030041. View

14.

Feng C, Tian C, Huang L, Chen H, Feng Y, Chang S . A Bibliometric Analysis of the Landscape of Parathyroid Carcinoma Research Based on the PubMed (2000-2021). Front Oncol. 2022; 12:824201. PMC: 8858798. DOI: 10.3389/fonc.2022.824201. View

15.

Zheng C, Xue J, Sun Y, Zhu T . Public Opinions and Concerns Regarding the Canadian Prime Minister's Daily COVID-19 Briefing: Longitudinal Study of YouTube Comments Using Machine Learning Techniques. J Med Internet Res. 2021; 23(2):e23957. PMC: 7903980. DOI: 10.2196/23957. View

16.

Riley W, Bibb K, Hargrave S, Fearon P . Publication rates from biomedical and behavioral and social science R01s funded by the National Institutes of Health. PLoS One. 2020; 15(11):e0242271. PMC: 7665634. DOI: 10.1371/journal.pone.0242271. View

17.

Hampshire A, Hellyer P, Trender W, Chamberlain S . Insights into the impact on daily life of the COVID-19 pandemic and effective coping strategies from free-text analysis of people's collective experiences. Interface Focus. 2021; 11(6):20210051. PMC: 8504891. DOI: 10.1098/rsfs.2021.0051. View

18.

Patel J, Desai H, Okhowat A . The Role of the Canadian Media During the Initial Response to the COVID-19 Pandemic: A Topic Modelling Approach Using Canadian Broadcasting Corporation News Articles. JMIR Infodemiology. 2021; 1(1):e25242. PMC: 8330922. DOI: 10.2196/25242. View

19.

Zhang Z, Yao L, Wang W, Jiang B, Xia F, Li X . A Bibliometric Analysis of 34,692 Publications on Thyroid Cancer by Machine Learning: How Much Has Been Done in the Past Three Decades?. Front Oncol. 2021; 11:673733. PMC: 8551832. DOI: 10.3389/fonc.2021.673733. View

20.

Cotfas L, Delcea C, Gherai R . COVID-19 Vaccine Hesitancy in the Month Following the Start of the Vaccination Process. Int J Environ Res Public Health. 2021; 18(19). PMC: 8508534. DOI: 10.3390/ijerph181910438. View