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A statistical analysis of vaccine-adverse event data

dc.contributor.authorRen, Jian-Jian
dc.contributor.authorSun, Tingni
dc.contributor.authorHe, Yongqun
dc.contributor.authorZhang, Yuji
dc.identifier.citationRen, JJ., Sun, T., He, Y. et al. A statistical analysis of vaccine-adverse event data. BMC Med Inform Decis Mak 19, 101 (2019).en_US
dc.description.abstractVaccination has been one of the most successful public health interventions to date, and the U.S. FDA/CDC Vaccine Adverse Event Reporting System (VAERS) currently contains more than 500,000 reports for post-vaccination adverse events that occur after the administration of vaccines licensed in the United States. The VAERS dataset is huge, contains very large dimension nominal variables, and is complex due to multiple listing of vaccines and adverse symptoms in a single report. So far there has not been any statistical analysis conducted in attempting to identify the cross-board patterns on how all reported adverse symptoms are related to the vaccines.en_US
dc.publisherSpringer Natureen_US
dc.subjectBacteria vaccineen_US
dc.subjectCorrelation coefficient matrixen_US
dc.subjectData visualizationen_US
dc.subjectInactivated vaccineen_US
dc.subjectLive vaccineen_US
dc.subjectNeighboring methoden_US
dc.subjectVirus vaccineen_US
dc.titleA statistical analysis of vaccine-adverse event dataen_US
dc.relation.isAvailableAtCollege of Computer, Mathematical & Natural Sciencesen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us

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