SARS-CoV-2 spread rapidly in the Brazilian Amazon and the attack rate there is an estimate of the final size of a largely unmitigated epidemic. We use a convenience sample of blood donors to show that by June, one month after the epidemic peak in Manaus, capital of Amazonas state, 44% of the population had detectable IgG antibodies. Correcting for cases without a detectable antibody response and antibody waning, we estimate a 66% attack rate in June, rising to 76% in October. This is higher than in Sao Paulo, in southeastern Brazil, where the estimated attack rate in October is 29%. These results confirm that, when poorly controlled, COVID-19 can infect a high fraction of the population causing high mortality.

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Three-quarters attack rate of SARS-CoV-2 in the Brazilian Amazon during a largely unmitigated epidemic.

Objective To estimate the infection fatality rate of coronavirus disease 2019 (COVID-19) from seroprevalence data. Methods I searched PubMed and preprint servers for COVID-19 seroprevalence studies with a sample size ≥ 500 as of 9 September 2020. I also retrieved additional results of national studies from preliminary press releases and reports. I assessed the studies for design features and seroprevalence estimates. I estimated the infection fatality rate for each study by dividing the cumulative number of COVID-19 deaths by the number of people estimated to be infected in each region. I corrected for the number of immunoglobin (Ig) types tested (IgG, IgM, IgA). Findings I included 61 studies (74 estimates) and eight preliminary national estimates. Seroprevalence estimates ranged from 0.02% to 53.40%. Infection fatality rates ranged from 0.00% to 1.63%, corrected values from 0.00% to 1.54%. Across 51 locations, the median COVID-19 infection fatality rate was 0.27% (corrected 0.23%): the rate was 0.09% in locations with COVID-19 population mortality rates less than the global average ( 500 COVID-19 deaths/million people. In people younger than 70 years, infection fatality rates ranged from 0.00% to 0.31% with crude and corrected medians of 0.05%. Conclusion The infection fatality rate of COVID-19 can vary substantially across different locations and this may reflect differences in population age structure and case-mix of infected and deceased patients and other factors. The inferred infection fatality rates tended to be much lower than estimates made earlier in the pandemic.

Infection fatality rate of COVID-19 inferred from seroprevalence data

Serological evidence of human infection with SARS-CoV-2: a systematic review and meta-analysis.

SARS-CoV-2 variant B.1.617 is resistant to bamlanivimab and evades antibodies induced by infection and vaccination.

SARS-CoV-2 antibody seroprevalence in India, August-September, 2020: findings from the second nationwide household serosurvey.

Seroprevalence of SARS-CoV-2 in slums versus non-slums in Mumbai, India.

Objective: Estimate seroprevalence in representative samples from slum and non-slum communities in Mumbai, India, a mega-city in a low or middle-income country and test if prevalence is different in slums. 

Design: After geographically-spaced community sampling of households, one individual per household was tested for IgG antibodies to SARS-CoV-2 N-protein in a two-week interval. 

Setting: Slum and non-slum communities in three wards, one each from the three main zones of Mumbai. 

Participants: Individuals over age 12 who consent to and have no contraindications to venipuncture were eligible. 6,904 participants (4,202 from slums and 2,702 from non-slums) were tested.

Main outcome measures: The primary outcomes were the positive test rate for IgG antibodies to the SARS-CoV-2 N-protein by demographic group (age and gender) and location (slums and non-slums). The secondary outcome is seroprevalence at slum and non-slum levels. Sera was tested via chemiluminescence (CLIA) using Abbott Diagnostics ArchitectTM N-protein based test. Seroprevalence was calculated using weights to match the population distribution by age and gender and accounting for imperfect sensitivity and specificity of the test.

Results: The positive test rate was 54.1% (95% CI: 52.7 to 55.6) and 16.1% (95% CI: 14.9 to 17.4) in slums and non-slums, respectively, a difference of 38 percentage points (P < 0.001). Accounting for imperfect accuracy of tests (e.g., sensitivity, 0.90; specificity 1.00), seroprevalence was as high as 58.4% (95% CI: 56.8 to 59.9) and 17.3% (95% CI: 16 to 18.7) in slums and non-slums, respectively. 

Conclusions: The high seroprevalence in slums implies a moderate infection fatality rate. The stark difference in seroprevalence across slums and non-slums has implications for the efficacy of social distancing, the level of herd immunity, and equity. It underlines the importance of geographic specificity and urban structure in modeling SARS-CoV-2.

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Seroprevalence of SARS-CoV-2 in slums and non-slums of Mumbai, India, during June 29-July 19, 2020

Article Summary : Antibodies waned after India’s first COVID wave and both vaccination 24 and infection contributed roughly 90% seroprevalence Abstract : Four rounds of serological surveys were conducted, spanning two COVID waves 27 (October 2020 and April-May 2021), in Tamil Nadu (population 72 million) state in India. Each 28 round included representative populations in each district of the state, totaling ≥ 20,000 persons 29 per round. State-level seroprevalence was 31.5% in round 1 (October-November 2020), after 30 India’s first COVID wave. Seroprevalence fell to 22.9% in 2 (April 2021), consistent with 31 waning of antibodies from natural infection. Seroprevalence rose to 67.1% by round 3 (June- 32 July 2021), reflecting infections from the Delta-variant induced second COVID wave. 33 Seroprevalence rose to 93.1% by round 4 (December 2021-January 2022), reflecting higher 34 vaccination rates. Antibodies also appear to wane after vaccination. Seroprevalence in urban 35 areas was higher than in rural areas, but the gap shrunk over time (35.7 v. 25.7% in round 1, 36 89.8% v. 91.4% in round 4) as the epidemic spread even in low-density rural areas.

Sofia Imad

Papers

Seroprevalence of SARS-CoV-2 in slums versus non-slums in Mumbai, India.

Seroprevalence of SARS-CoV-2 in slums and non-slums of Mumbai, India, during June 29-July 19, 2020

Contribution of infection and vaccination to seroprevalence 1 through two COVID waves in Tamil Nadu, India