Utilizing virus genomic surveillance to predict vaccine effectiveness.
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| Title: | Utilizing virus genomic surveillance to predict vaccine effectiveness. |
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| Authors: | Kwon J; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America.; Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut, United States of America., Li K; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America.; Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut, United States of America., Warren JL; Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut, United States of America.; Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America., Pandya S; Department of Laboratory Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America.; Yale School of Medicine Biorepository, Yale University, New Haven, Connecticut, United States of America., Hahn AM; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America.; Department of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia., Pitzer VE; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America.; Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut, United States of America., Weinberger DM; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America.; Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut, United States of America., Grubaugh ND; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America.; Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut, United States of America.; Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut, United States of America. |
| Corporate Authors: | Yale SARS-CoV-2 Genomic Surveillance Initiative |
| Source: | PLoS computational biology [PLoS Comput Biol] 2026 May 26; Vol. 22 (5), pp. e1014329. Date of Electronic Publication: 2026 May 26 (Print Publication: 2026). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1553-7358 |
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| DOI: | 10.1371/journal.pcbi.1014329 |
