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. 2022 Sep:233:106551.
doi: 10.1016/j.actatropica.2022.106551. Epub 2022 Jun 9.

Seroprevalence of SARS-CoV-2 on health professionals via Bayesian estimation: a Brazilian case study before and after vaccines

Caio B S Maior  1 , Isis D Lins  2 , Leonardo S Raupp  3 , Márcio C Moura  3 , Felipe Felipe  3 , João M M Santana  3 , Mariana P Fernandes  4 , Alice V Araújo  5 , Ana L V Gomes  6
Affiliations

Seroprevalence of SARS-CoV-2 on health professionals via Bayesian estimation: a Brazilian case study before and after vaccines

Caio B S Maior et al. Acta Trop. 2022 Sep.

Abstract

The increasing number of COVID-19 infections brought by the current pandemic has encouraged the scientific community to analyze the seroprevalence in populations to support health policies. In this context, accurate estimations of SARS-CoV-2 antibodies based on antibody tests metrics (e.g., specificity and sensitivity) and the study of population characteristics are essential. Here, we propose a Bayesian analysis using IgA and IgG antibody levels through multiple scenarios regarding data availability from different information sources to estimate the seroprevalence of health professionals in a Northeastern Brazilian city: no data available, data only related to the test performance, data from other regions. The study population comprises 432 subjects with more than 620 collections analyzed via IgA/IgG ELISA tests. We conducted the study in pre- and post-vaccination campaigns started in Brazil. We discuss the importance of aggregating available data from various sources to create informative prior knowledge. Considering prior information from the USA and Europe, the pre-vaccine seroprevalence means are 8.04% and 10.09% for IgG and 7.40% and 9.11% for IgA. For the post-vaccination campaign and considering local informative prior, the median is 84.83% for IgG, which confirms a sharp increase in the seroprevalence after vaccination. Additionally, stratification considering differences in sex, age (younger than 30 years, between 30 and 49 years, and older than 49 years), and presence of comorbidities are provided for all scenarios.

Keywords: Bayesian inference; COVID-19; Databases; Serological Diagnosis; Seroprevalence.

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Conflict of interest statement

None.

Figures

Fig. 1
Fig. 1
Chronological collection of samples in both databases.
Fig. 2
Fig. 2
(A)IgG and (B)IgA results from Database 1. (C) Convergence of results from IgG and IgA tests for the same sample.
Fig. 3
Fig. 3
IgG classification for Database 2.
Fig. 4
Fig. 4
Results using pre-vaccine data (Database 1) for IgG, cases 1-4.
Fig. 5
Fig. 5
Results using post-vaccine data (Database 2) for IgG, cases 5-7.
See this image and copyright information in PMC

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