A Bayesian estimate of the early COVID-19 infection fatality ratio in Brazil based on a random seroprevalence survey.


Journal

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases
ISSN: 1878-3511
Titre abrégé: Int J Infect Dis
Pays: Canada
ID NLM: 9610933

Informations de publication

Date de publication:
Oct 2021
Historique:
received: 06 04 2021
revised: 15 07 2021
accepted: 06 08 2021
pubmed: 15 8 2021
medline: 8 10 2021
entrez: 14 8 2021
Statut: ppublish

Résumé

A number of estimates of the infection fatality ratio (IFR) of SARS-CoV-2 in different countries have been published. In Brazil, the fragile political situation, together with socioeconomic and ethnic diversity, could result in substantially different IFR estimates. We infer the IFR in Brazil in 2020 by combining three datasets. We compute the prevalence via the population-based seroprevalence survey, EPICOVID19-BR. For the fatalities we obtain the absolute number using the public Painel Coronavírus dataset and the age-relative number using the public SIVEP-Gripe dataset. The time delay between the development of antibodies and subsequent fatality is estimated via the SIVEP-Gripe dataset. We obtain the IFR for each survey stage and 27 federal states. We include the effect of fading IgG antibody levels by marginalizing over the test detectability time window. We infer a country-wide average IFR (maximum posterior and 95% CI) of 1.03% (0.88-1.22%) and age-specific IFRs of 0.032% (0.023-0.041%) [< 30 years], 0.22% (0.18-0.27%) [30-49 years], 1.2% (1.0-1.5%) [50-69 years], and 3.0% (2.4-3.9%) [≥ 70 years]. We find that the fatality ratio in the country increased significantly at the end of June 2020, likely due to the increased strain on the health system. Our IFR estimate is based on data and does not rely on extrapolating models. This estimate sets a baseline value with which future medications and treatment protocols may be confronted.

Sections du résumé

BACKGROUND BACKGROUND
A number of estimates of the infection fatality ratio (IFR) of SARS-CoV-2 in different countries have been published. In Brazil, the fragile political situation, together with socioeconomic and ethnic diversity, could result in substantially different IFR estimates.
METHODS METHODS
We infer the IFR in Brazil in 2020 by combining three datasets. We compute the prevalence via the population-based seroprevalence survey, EPICOVID19-BR. For the fatalities we obtain the absolute number using the public Painel Coronavírus dataset and the age-relative number using the public SIVEP-Gripe dataset. The time delay between the development of antibodies and subsequent fatality is estimated via the SIVEP-Gripe dataset. We obtain the IFR for each survey stage and 27 federal states. We include the effect of fading IgG antibody levels by marginalizing over the test detectability time window.
RESULTS RESULTS
We infer a country-wide average IFR (maximum posterior and 95% CI) of 1.03% (0.88-1.22%) and age-specific IFRs of 0.032% (0.023-0.041%) [< 30 years], 0.22% (0.18-0.27%) [30-49 years], 1.2% (1.0-1.5%) [50-69 years], and 3.0% (2.4-3.9%) [≥ 70 years]. We find that the fatality ratio in the country increased significantly at the end of June 2020, likely due to the increased strain on the health system.
CONCLUSIONS CONCLUSIONS
Our IFR estimate is based on data and does not rely on extrapolating models. This estimate sets a baseline value with which future medications and treatment protocols may be confronted.

Identifiants

pubmed: 34390858
pii: S1201-9712(21)00648-2
doi: 10.1016/j.ijid.2021.08.016
pmc: PMC8358085
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

190-195

Informations de copyright

Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.

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Auteurs

Valerio Marra (V)

Núcleo de Astrofísica e Cosmologia & Departamento de Física, Universidade Federal do Espírito Santo, Vitória, ES, Brazil.

Miguel Quartin (M)

Instituto de Física & Observatório do Valongo, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil. Electronic address: mquartin@if.ufrj.br.

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Classifications MeSH