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Population immunity to SARS-CoV-2 in US states and counties due to infection and vaccination, January 2020-November 2021
Fayette Klaassen; Melanie H Chitwood; Ted Cohen; Virginia E Pitzer; Marcus Russi; Nicole A Swartwood; Joshua A Salomon; Nicolas A Menzies.
Afiliación
  • Fayette Klaassen; Harvard
  • Melanie H Chitwood; Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven CT
  • Ted Cohen; Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven CT
  • Virginia E Pitzer; Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven CT
  • Marcus Russi; Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven CT
  • Nicole A Swartwood; Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston MA
  • Joshua A Salomon; Department of Health Policy, Stanford University School of Medicine, Stanford CA.
  • Nicolas A Menzies; Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston MA
Preprint en En | PREPRINT-MEDRXIV | ID: ppmedrxiv-21268272
ABSTRACT
Prior infection and vaccination both contribute to population-level SARS-CoV-2 immunity. We used a Bayesian model to synthesize evidence and estimate population immunity to prevalent SARS-CoV-2 variants in the United States over the course of the epidemic until December 1, 2021, and how this changed with the introduction of the Omicron variant. We used daily SARS-CoV-2 infection estimates and vaccination coverage data for each US state and county. We estimated relative rates of vaccination conditional on previous infection status using the Census Bureaus Household Pulse Survey. We used published evidence on natural and vaccine-induced immunity, including waning and immune escape. The estimated percentage of the US population with a history of SARS-CoV-2 infection or vaccination as of December 1, 2021, was 88.2% (95%CrI 83.6%-93.5%), compared to 24.9% (95%CrI 18.5%-34.1%) on January 1, 2021. State-level estimates for December 1, 2021, ranged between 76.9% (95%CrI 67.6%-87.6%, West Virginia) and 94.4% (95%CrI 91.2%-97.3%, New Mexico). Accounting for waning and immune escape, the effective protection against the Omicron variant on December 1, 2021, was 21.8% (95%CrI 20.7%-23.4%) nationally and ranged between 14.4% (95%CrI 13.2%-15.8%, West Virginia), to 26.4% (95%CrI 25.3%-27.8%, Colorado). Effective protection against severe disease from Omicron was 61.2% (95%CrI 59.1%-64.0%) nationally and ranged between 53.0% (95%CrI 47.3%-60.0%, Vermont) and 65.8% (95%CrI 64.9%-66.7%, Colorado). While over three-quarters of the US population had prior immunological exposure to SARS-CoV-2 via vaccination or infection on December 1, 2021, only a fifth of the population was estimated to have effective protection to infection with the immune-evading Omicron variant. SignificanceBoth SARS-CoV-2 infection and COVID-19 vaccination contribute to population-level immunity against SARS-CoV-2. This study estimates the immunity and effective protection against future SARS-CoV-2 infection in each US state and county over 2020-2021. The estimated percentage of the US population with a history of SARS-CoV-2 infection or vaccination as of December 1, 2021, was 88.2% (95%CrI 83.6%-93.5%). Accounting for waning and immune escape, protection against the Omicron variant was 21.8% (95%CrI 20.7%-23.4%). Protection against infection with the Omicron variant ranged between 14.4% (95%CrI 13.2%-15.8%%, West Virginia) and 26.4% (95%CrI 25.3%-27.8%, Colorado) across US states. The introduction of the immune-evading Omicron variant resulted in an effective absolute increase of approximately 30 percentage points in the fraction of the population susceptible to infection.
Licencia
cc_by_nc
Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-MEDRXIV Tipo de estudio: Observational_studies / Prognostic_studies Idioma: En Año: 2021 Tipo del documento: Preprint
Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-MEDRXIV Tipo de estudio: Observational_studies / Prognostic_studies Idioma: En Año: 2021 Tipo del documento: Preprint