A Bayesian estimate of the early COVID-19 infection fatality ratio in Brazil based on a random seroprevalence survey
Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved..
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: 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: 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: 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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:111 |
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| Enthalten in: |
International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases - 111(2021) vom: 02. Okt., Seite 190-195 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Marra, Valerio [VerfasserIn] |
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| Anmerkungen: |
Date Completed 07.10.2021 Date Revised 03.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.ijid.2021.08.016 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM329346113 |
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| 245 | 1 | 2 | |a A Bayesian estimate of the early COVID-19 infection fatality ratio in Brazil based on a random seroprevalence survey |
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| 500 | |a Date Revised 03.04.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
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