ABSTRACT
Policymakers in Africa need robust estimates of the current and future spread of SARS-CoV-2. We used national surveillance PCR test, serological survey and mobility data to develop and fit a county-specific transmission model for Kenya up to the end of September 2020, which encompasses the first wave of SARS-CoV-2 transmission in the country. We estimate that the first wave of the SARS-CoV-2 pandemic peaked before the end of July 2020 in the major urban counties, with 30-50% of residents infected. Our analysis suggests, first, that the reported low COVID-19 disease burden in Kenya cannot be explained solely by limited spread of the virus, and second, that a 30-50% attack rate was not sufficient to avoid a further wave of transmission.
ABSTRACT
Introduction/ Background: Previous reports have estimated national seroprevalence of anti-SARS-CoV-2 IgG antibodies among blood donors in Kenya at 4.3% (April-June 2020), 9.1% (August-September 2020), and 48.5% (January-March 2021). Here we describe seroprevalence in the period June-August 2021 when COVID-19 vaccine coverage was 2.5% in the adult population in Kenya. Methods: We undertook a cross-sectional descriptive study to estimate prevalence of anti-SARS-CoV-2 IgG antibodies using residual plasma from the 6 regional transfusion centres in Kenya. Samples with complete donor demographic data were included and analysed using an anti-spike IgG enzyme- linked immunosorbent assay with validated specificity of 99.0% and sensitivity of 92.7%. Bayesian multilevel regression with poststratification was used to obtain seroprevalence estimates and 95% credible intervals (Crl) adjusted for age, sex, and region of residence referenced against national 2019 census data for individuals aged 16-64 years. Results were also adjusted for test performance. Results: Of 7601 available plasma samples donated between 2nd June 2021 and 7th August 2021, 7139 (93.1%) were included in the analysis. Males comprised 5555 (78.8%) of the study population, while 4304 (60.3%) samples were from individuals aged 16-24 years. Crude seroprevalence was 67.2% (95% CrI, 66.1%- 68.3%). Overall Bayesian population-weighted, test-adjusted seroprevalence was 73.2% (95%CrI, 69.8-77.2%). Seroprevalence ranged from 69.9% among donors aged 35-44 years to 76.3% in the 16-24-year age group. We found no difference in seroprevalence by sex. Regional seroprevalence ranged from 58.9% in the coastal region (excluding Mombasa) to 82.5% in Nairobi. Impact: Local governments are making efforts to improve vaccine coverage while faced with limited access to the vaccines. These findings may guide targeted vaccine deployment through prioritization of vulnerable populations with lower seropositivity. Conclusion: SARS-CoV-2 has continued to spread rapidly across Kenya, infecting three-quarters of the adult population sampled through blood donation. The high seroprevalence observed is consistent with reports from other regions in sub-Saharan Africa and implies substantial infection-induced immunity that may mitigate the impact of low vaccine coverage.
ABSTRACT
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.