Quantifying previous SARS-CoV-2 infection through mixture modelling of antibody levels

Bottomley, Christian; Otiende, Mark; Uyoga, Sophie; Gallagher, Katherine E.; Kagucia, E. Wangeci; Etyang, Anthony; Mugo, Daisy; Gitonga, John N.; Karanja, Henry; Nyagwange, James; Adetifa, Ifedayo; Agweyu, Ambrose; Nokes, D. James; Warimwe, George M.; Scott, J. Anthony G.

doi:10.1038/s41467-021-26452-z

Cited by 20 publications

(20 citation statements)

References 32 publications

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“…22,23 A key limitation of the study is that we were unable to fully account for waning of antibodies, which could have led to an underestimation of seroprevalence. In a previous study, we undertook mixture modelling, 24 which does not rely on thresholds, but instead assumes that the population sampled consists of two groups with different distributions of antibody levels; this suggested that the threshold-based method was significantly underestimating the true seroprevalence. We were however unable to conduct similar analysis for this dataset, as the mixture modelling requires control data from the respective sites which we did not have.…”

Section: Discussionmentioning

confidence: 99%

SARS-CoV-2 Seroprevalence in three Kenyan Health and Demographic Surveillance Sites, December 2020-May 2021

Etyang

Adetifa

Omore

et al. 2022

Preprint

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Importance Most of the studies that have informed the public health response to the COVID-19 pandemic in Kenya have relied on samples that are not representative of the general population. Objective To determine the cumulative incidence of infection with SARS-CoV-2, from a randomly selected sample of individuals normally resident at three Health and Demographic Surveillance Systems (HDSSs) in Kenya. Design This was a cross-sectional population-based serosurvey conducted at Kilifi HDSS, Nairobi Urban HDSS, and Manyatta HDSS in Kenya. We selected age-stratified samples at HDSSs in Kilifi, Kisumu and Nairobi, in Kenya. Blood samples were collected from participants between 01 Dec 2020 and 27 May 2021. Setting Kilifi HDSS comprises a predominantly rural population, Manyatta HDSS comprises a predominantly semi-urban population, while Nairobi Urban HDSS comprises an urban population. The total population under regular surveillance at the three sites is ~470,000. Exposure We tested for IgG antibodies to SARS-CoV-2 spike protein using ELISA. Locally validated assay sensitivity and specificity were 93% (95% CI 88-96%) and 99% (95% CI 98-99.5%), respectively. Main Outcome and Measures The primary outcome measure was cumulative incidence of infection with SARS-COV-2 virus as evidenced by seropositivity to SARS-CoV-2 whole spike protein. We adjusted our estimates using classical methods and Bayesian modelling to account for assay performance. We performed multivariable logistic regression to test associations between seropositivity and age category, time period and sex. Results We recruited 2,559 individuals from the three HDSS sites, median age (IQR) 27years (10-78) and 52% were female. Seroprevalence at all three sites rose steadily during the study period. In Kilifi, Kisumu and Nairobi, seroprevalences at the beginning of the study were 14.5 % (9.1-21), 36.0 (28.2-44.4) and 32.4 % (23.1-42.4) respectively; at the end they were 27.6 % (21.4-33.9), 42.0 % (34.7-50.0) and 50.2 % (39.7-61.1), respectively. In multivariable logistic regression models that adjusted for sex and period of sample collections, age category was strongly associated with seroprevalence (p<0.001), with the highest seroprevalences being observed in the 35-44 and ≥65 year age categories. Conclusion There has been substantial unobserved transmission of SARS-CoV-2 in the general population in Kenya. There is wide variation in cumulative incidence by location and age category.

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Section: Discussionmentioning

confidence: 99%

SARS-CoV-2 Seroprevalence in three Kenyan Health and Demographic Surveillance Sites, December 2020-May 2021

Etyang

Adetifa

Omore

et al. 2022

Preprint

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“…Given these limitations, mixture models have been proposed 19 and used previously to perform inference on serological data [20][21][22] . A major strength of these approaches is that they infer seropositivity using the distribution of raw antibody values in the population, obviating the need for a strict, binary cut-off.…”

Section: Discussionmentioning

confidence: 99%

A mixture model to estimate SARS-CoV-2 seroprevalence in Chennai, India

Hitchings

Patel

Khan

et al. 2022

Preprint

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Serological assays used to estimate SARS-CoV-2 seroprevalence rely on manufacturer cut-offs established based on more severe early cases who tended to be older. We conducted a household-based serosurvey of 4,677 individuals from 2,619 households in Chennai, India from January to May, 2021. Samples were tested for SARS-CoV-2 IgG antibodies to the spike (S) and nucelocapsid (N) proteins. We calculated seroprevalence using manufacturer cut-offs and using a mixture model in which individuals were assigned a probability of being seropositive based on their measured IgG, accounting for heterogeneous antibody response across individuals. The SARS-CoV-2 seroprevalence to anti-S and anti-N IgG was 62.0% (95% confidence interval [CI], 60.6 to 63.4) and 13.5% (95% CI, 12.6 to 14.5), respectively applying the manufacturer's cut-offs, with low inter-assay agreement (Cohen's kappa 0.15). With the mixture model, estimated anti-S IgG and anti-N IgG seroprevalence was 64.9% (95% Credible Interval [CrI], 63.8 to 66.0) and 51.5% (95% CrI, 50.2 to 52.9) respectively, with high inter-assay agreement (Cohen's kappa 0.66). Age and socioeconomic factors showed inconsistent relationships with anti-S IgG and anti-N IgG seropositivity using manufacturer's cut-offs, but the mixture model reconciled these differences. In the mixture model, age was not associated with seropositivity, and improved household ventilation was associated with lower seropositivity odds. With global vaccine scale-up, the utility of the more stable anti-S IgG assay may be limited due to the inclusion of the S protein in several vaccines. SARS-CoV-2 seroprevalence estimates using alternative targets must consider heterogeneity in seroresponse to ensure seroprevalence is not underestimated and correlates not misinterpreted.

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“…Seroprevalence is currently highest in Nairobi, then Busia, then Kilifi, correlating with the counties' population densities. In Nairobi, in August 2020, just after the peak of the first wave of SARS-CoV-2 infections, mixture modelling, which attempts to account for the wide range of OD ratios among those exposed to the virus better than the simple threshold analysis (20), indicates a cumulative incidence of 75%, just 4 months after the start of the pandemic. At the same timepoint, 6,727 PCR-confirmed infections had been registered across the city (<1% of the County's population; Supplementary Figure 2).…”

Section: Discussionmentioning

confidence: 99%

Sero-surveillance for IgG to SARS-CoV-2 at antenatal care clinics in three Kenyan referral hospitals: repeated cross-sectional surveys 2020-21

Lucinde

Mugo

Bottomley

et al. 2022

Preprint

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IntroductionThe high proportion of SARS-CoV-2 infections that have remained undetected presents a challenge to tracking the progress of the pandemic and estimating the extent of population immunity.MethodsWe used residual blood samples from women attending antenatal care services at three hospitals in Kenya between August 2020 and October 2021and a validated IgG ELISA for SARS-Cov-2 spike protein and adjusted the results for assay sensitivity and specificity. We fitted a two-component mixture model as an alternative to the threshold analysis to estimate of the proportion of individuals with past SARS-CoV-2 infection.ResultsWe estimated seroprevalence in 2,981 women; 706 in Nairobi, 567 in Busia and 1,708 in Kilifi. By October 2021, 13% of participants were vaccinated (at least one dose) in Nairobi, 2% in Busia. Adjusted seroprevalence rose in all sites; from 50% (95%CI 42-58) in August 2020, to 85% (95%CI 78-92) in October 2021 in Nairobi; from 31% (95%CI 25-37) in May 2021 to 71% (95%CI 64-77) in October 2021 in Busia; and from 1% (95% CI 0-3) in September 2020 to 63% (95% CI 56-69) in October 2021 in Kilifi. Mixture modelling, suggests adjusted cross-sectional prevalence estimates are underestimates; seroprevalence in October 2021 could be 74% in Busia and 72% in Kilifi.ConclusionsThere has been substantial, unobserved transmission of SARS-CoV-2 in Nairobi, Busia and Kilifi Counties. Due to the length of time since the beginning of the pandemic, repeated cross-sectional surveys are now difficult to interpret without the use of models to account for antibody waning.

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Quantifying previous SARS-CoV-2 infection through mixture modelling of antibody levels

Cited by 20 publications

References 32 publications

SARS-CoV-2 Seroprevalence in three Kenyan Health and Demographic Surveillance Sites, December 2020-May 2021

SARS-CoV-2 Seroprevalence in three Kenyan Health and Demographic Surveillance Sites, December 2020-May 2021

A mixture model to estimate SARS-CoV-2 seroprevalence in Chennai, India

Sero-surveillance for IgG to SARS-CoV-2 at antenatal care clinics in three Kenyan referral hospitals: repeated cross-sectional surveys 2020-21

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