Please use this identifier to cite or link to this item: http://nopr.niscpr.res.in/handle/123456789/61835
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dc.contributor.authorDeshpande, Nishad-
dc.contributor.authorLigade, Virendra-
dc.contributor.authorShaikh, Shabib-Ahmed-
dc.contributor.authorKhode, Alok-
dc.date.accessioned2023-05-03T08:42:58Z-
dc.date.available2023-05-03T08:42:58Z-
dc.date.issued2023-05-
dc.identifier.issn0975-2404 (Online); 0972-5423 (Print)-
dc.identifier.urihttp://nopr.niscpr.res.in/handle/123456789/61835-
dc.description41-51en_US
dc.description.abstractTwo thousand one hundred and ninety-eight research publications on COVID-19 vaccines in MedRxiv preprint repository during January 01, 2020 and December 31, 2021 were analyzed for topic modelling with unsupervised inference method. Latent Dirichlet Allocation (LDA) method was used to investigate the thematic structure of the preprints. It was observed that the published articles were related to either clinical trials or patient responses to vaccine or modelling for various applications such as infection transmission, vaccine allocation, vaccine hesitancy etc.en_US
dc.language.isoenen_US
dc.publisherNIScPR-CSIR, Indiaen_US
dc.sourceALIS Vol.70(1) [March 2023]en_US
dc.subjectCOVID-19en_US
dc.subjectVaccineen_US
dc.subjectPreprintsen_US
dc.subjectLDAen_US
dc.subjectTopic modellingen_US
dc.titleTopic modelling-based analysis of COVID-19 vaccine articles published in the preprint server MedRxiven_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.56042/alis.v70i1.71939en_US
Appears in Collections:ALIS Vol.70(1) [March 2023]

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