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A Bayesian Estimate of the COVID-19 Infection Fatality Ratio in Brazil Based on a Random Seroprevalence Survey
15 Pages Posted: 2 Mar 2021
More...Abstract
Background: The COVID-19 pandemic continues to spread fast through Brazil after almost a year since the first fatality. Brazil, in addition to a fragile political situation, has a very socioeconomically and ethnically diverse population for which estimates of the infection fatality ratio (IFR) of SARS-CoV-2 relative to other countries may not be accurate. It is then important to estimate the IFR of SARS-CoV-2 for the Brazilian federal states as well as for Brazil as a whole.
Methods: 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 via Bayesian inference for each survey stage and 27 federal states. We include the effect of fading IgG antibody levels by marginalizing over the time after contagion at which the test gives a negative result with a flat prior on the interval [40,80] days.
Findings: We infer a country-wide average IFR (maximum posterior and 95% CI) of 0.97% (0.82-1.14%) and age-specific IFR: 0.028% (0.024-0.036%) [< 30 years], 0.21% (0.17-0.25%) [30-49 years], 1.06% (0.88-1.31%) [50-69 years], 2.9% (2.5-3.7%) [≥ 70 years].
Interpretation: Our IFR estimate is the most accurate to date for Brazil and one of the most detailed and careful estimates globally. It is based on data and does not rely on extrapolating models, and Bayesian inference is rigorously adopted. This estimate sets a baseline value with which future medications and treatment protocols may be confronted, especially with the rise of recent SARS-CoV-2 variants and the beginning of mass vaccination campaigns. The high IFR values found and its age-dependence generate actionable results.
Funding: No funding was granted specifically for this project.
Declaration of Interests: The authors declare no competing interests.
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