National- and State-level SARS-CoV-2 Immunity Trends From January 2020 to December 2023: a Mathematical Modeling Analysis.

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Bibliographic Details
Title: National- and State-level SARS-CoV-2 Immunity Trends From January 2020 to December 2023: a Mathematical Modeling Analysis.
Authors: Klaassen F; Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA., Swartwood NA; Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA., Chitwood MH; Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut, USA., Lopes R; Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut, USA., Haraguchi M; Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut, USA., Salomon JA; Department of Health Policy, Stanford University School of Medicine, Stanford, California, USA., Cohen T; Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut, USA., Menzies NA; Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Source: The Journal of infectious diseases [J Infect Dis] 2026 Apr 29; Vol. 233 (4), pp. 714-724.
Publication Type: Journal Article
Journal Info: Publisher: Oxford University Press Country of Publication: United States NLM ID: 0413675 Publication Model: Print Cited Medium: Internet ISSN: 1537-6613 (Electronic) Linking ISSN: 00221899 NLM ISO Abbreviation: J Infect Dis Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1537-6613
DOI:10.1093/infdis/jiaf532