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As countries decide on vaccination strategies and how to ease movement restrictions, estimating the proportion of the population previously infected with SARS-CoV-2 is important for predicting the future burden of COVID-19. This proportion is usually estimated from serosurvey data in two steps: first the proportion above a threshold antibody level is calculated, then the crude estimate is adjusted using external estimates of sensitivity and specificity. A drawback of this approach is that the PCR-confirmed cases used to estimate the sensitivity of the threshold may not be representative of cases in the wider population—e.g., they may be more recently infected and more severely symptomatic. Mixture modelling offers an alternative approach that does not require external data from PCR-confirmed cases. Here we illustrate the bias in the standard threshold-based approach by comparing both approaches using data from several Kenyan serosurveys. We show that the mixture model analysis produces estimates of previous infection that are often substantially higher than the standard threshold analysis., The proportion of a population that has previously been infected by a pathogen is typically estimated using antibody thresholds adjusted for sensitivity and specificity. Here, the authors present a model-based alternative to threshold methods which accounts for antibody waning and other sources of spectrum bias.

Observed SARS-CoV-2 infections and deaths are low in tropical Africa raising questions about the extent of transmission. We measured SARS-CoV-2 IgG by ELISA in 9,922 blood donors across Kenya and adjusted for sampling bias and test performance. By 1st September 2020, 577 COVID-19 deaths were observed nationwide and seroprevalence was 9.1% (95%CI 7.6-10.8%). Seroprevalence in Nairobi was 22.7% (18.0-27.7%). Although most people remained susceptible, SARS-CoV-2 had spread widely in Kenya with apparently low associated mortality., The reported burden of SARS-CoV-2 has been relatively low in tropical Africa compared to Europe and the Americas, but estimating true infection rates is challenging. Here, the authors screen blood donors in Kenya for SARS-CoV-2 antibodies and describe spatiotemporal seroprevalence dynamics.

The high proportion of SARS-CoV-2 infections that remain undetected presents a challenge to tracking the progress of the pandemic and implementing control measures in Kenya. We determined the prevalence of IgG to SARS-CoV-2 in residual blood samples from mothers attending antenatal care services at 2 referral hospitals in Kenya. We used a validated IgG ELISA for SARS-Cov-2 spike protein and adjusted the results for assay sensitivity and specificity. In Kenyatta National Hospital, Nairobi, seroprevalence in August 2020 was 49.9% (95% CI 42.7-58.0). In Kilifi County Hospital seroprevalence increased from 1.3% (95% CI 0.04-4.7) in September to 11.0% (95% CI 6.2-16.7) in November 2020. There has been substantial, unobserved transmission of SARS-CoV-2 in parts of Nairobi and Kilifi Counties.

BackgroundThere are no data on SARS-CoV-2 seroprevalence in Africa though the COVID-19 epidemic curve and reported mortality differ from patterns seen elsewhere. We estimated the anti-SARS-CoV-2 antibody prevalence among blood donors in Kenya.MethodsWe measured anti-SARS-CoV-2 spike IgG prevalence by ELISA on residual blood donor samples obtained between April 30 and June 16, 2020. Assay sensitivity and specificity were 83% (95% CI 59-96%) and 99.0% (95% CI 98.1-99.5%), respectively. National seroprevalence was estimated using Bayesian multilevel regression and post-stratification to account for non-random sampling with respect to age, sex and region, adjusted for assay performance.ResultsComplete data were available for 3098 of 3174 donors, aged 15-64 years. By comparison with the Kenyan population, the sample over- represented males (82% versus 49%), adults aged 25-34 years (40% versus 27%) and residents of coastal Counties (49% versus 9%). Crude overall seroprevalence was 5.6% (174/3098). Population-weighted, test- adjusted national seroprevalence was 5.2% (95% CI 3.7– 7.1%). Seroprevalence was highest in the 3 largest urban Counties - Mombasa (9.3% [95% CI 6.4-13.2%)], Nairobi (8.5% [95% CI 4.9-13.5%]) and Kisumu (6.5% [95% CI 3.3-11.2%]).ConclusionsWe estimate that 1 in 20 adults in Kenya had SARS-CoV-2 antibodies during the study period. By the median date of our survey, only 2093 COVID-19 cases and 71 deaths had been reported through the national screening system. This contrasts, by several orders of magnitude, with the numbers of cases and deaths reported in parts of Europe and America when seroprevalence was similar.

Background Estimating vaccination coverage and delays are important because these measures can identify at risk sub-populations who can be targeted with interventions and public health policies. This paper sought to determine estimates and risk factors for children in rural western Kenya who did not receive immunization, received immunization with delay, or were severely underimmunized. Methods Caregivers of children aged 12–23 months old were surveyed for immunization history using written records from the immunization booklet. Risk factors for not receiving immunization, delayed immunization, and severe underimmunization were calculated using log-binomial regression. Children were categorized as delayed if a given immunization was received greater than four weeks from the age-appropriate scheduled date. Severely underimmunized children were those who were fully unvaccinated for more than 90 days and had three or more vaccines delayed or not given. Results Immunization coverage for pentavalent1, pentavalent3, measles, and fully immunized child (FIC; BCG, three doses of polio, three doses of pentavalent, and measles vaccines) were 99%, 94%, 83%, and 80%, respectively. Approximately, 10%, 24%, and 29%, of children were delayed for pentavalent1, pentavalent3, and measles, respectively. Each model produced a unique combination of risk factors with only advanced maternal age as a risk factor common to all models. Children with delayed receipt of pentavalent1 were at risk for not receiving pentavalent3 (RR: 5.20; 95%CI 3.48, 7.77), measles vaccine (RR: 1.48; 95%CI 1.12, 1.95), and not achieving FIC (RR: 1.88; 95%CI 1.51, 2.34) compared with children who received pentavalent1 on time. Conclusions Immunization coverage among 12–23 month old children was high, yet a substantial proportion of children were vaccinated with delay. Although vaccine coverage and timeliness are often conceptualized as separate measures, the finding that delayed pentavalent1 receipt was a strong risk factor for not receiving future immunizations indicates the two measures are intertwined.