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Predicting SARS-CoV-2 variant spread in a completely seropositive population using semi-quantitative antibody measurements in blood donors

Title: Predicting SARS-CoV-2 variant spread in a completely seropositive population using semi-quantitative antibody measurements in blood donors
Authors: Buss, L
Prete, CA
Whittaker, C
Salomon, T
Oikawa, MK
Pereira, RHM
Moura, ICG
Delerino, L
Franca, RFO
Miyajima, F
Mendrone Jr, A
Almeida-Neto, C
Salles, NA
Ferreira, SC
Fladzinski, KA
De Souza, LM
Schier, LK
Inoue, PM
Xabregas, LA
Crispim, MAE
Fraiji, N
Carlos, LMB
Pessoa, V
Ribeiro, MA
De Souza, RE
Cavalcante, AF
Valenca, MIB
Da Silva, M
Lopes, E
Filho, LA
Mateos, SOG
Nunes, GT
Schlesinger, D
Nunes da Silva, SM
Silva-Junior, AL
Castro, MC
Nascimento, VH
Dye, C
Busch, MP
Faria, NR
Sabino, EC
Item Type: Journal Article
Abstract: SARS-CoV-2 serologic surveys estimate the proportion of the population with antibodies against historical variants, which nears 100% in many settings. New approaches are required to fully exploit serosurvey data. Using a SARS-CoV-2 anti-Spike (S) protein chemiluminescent microparticle assay, we attained a semi-quantitative measurement of population IgG titers in serial cross-sectional monthly samples of blood donations across seven Brazilian state capitals (March 2021–November 2021). Using an ecological analysis, we assessed the contributions of prior attack rate and vaccination to antibody titer. We compared anti-S titer across the seven cities during the growth phase of the Delta variant and used this to predict the resulting age-standardized incidence of severe COVID-19 cases. We tested ~780 samples per month, per location. Seroprevalence rose to >95% across all seven capitals by November 2021. Driven by vaccination, mean antibody titer increased 16-fold over the study, with the greatest increases occurring in cities with the highest prior attack rates. Mean anti-S IgG was strongly correlated (adjusted R2 = 0.89) with the number of severe cases caused by Delta. Semi-quantitative anti-S antibody titers are informative about prior exposure and vaccination coverage and may also indicate the potential impact of future SARS-CoV-2 variants.
Issue Date: 1-Sep-2022
Date of Acceptance: 24-Aug-2022
URI: http://hdl.handle.net/10044/1/101650
DOI: 10.3390/vaccines10091437
ISSN: 2076-393X
Publisher: MDPI AG
Start Page: 1
End Page: 11
Journal / Book Title: Vaccines
Volume: 10
Issue: 9
Copyright Statement: Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Publication Status: Published
Article Number: ARTN 1437
Online Publication Date: 2022-08-31
Appears in Collections:Department of Infectious Diseases
Imperial College London COVID-19
School of Public Health



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