Revealing the extent of the first wave of the COVID-19 pandemic in Kenya based on serological and PCR-test data

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.

Epidemiology of COVID-19 infections on routine polymerase chain reaction (PCR) and serology testing in Coastal Kenya.

Background: There are limited studies in Africa describing the epidemiology, clinical characteristics and serostatus of individuals tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We tested routine samples from the Coastal part of Kenya between 17 th March 2020 and 30 th June 2021. Methods: SARS-CoV-2 infections identified using reverse transcription polymerase chain reaction (RT-PCR) and clinical surveillance data at the point of sample collection were used to classify as either symptomatic or asymptomatic. IgG antibodies were measured in sera samples, using a well validated in-house enzyme-linked immunosorbent assay (ELISA). Results: Mombasa accounted for 56.2% of all the 99,694 naso-pharyngeal/oro-pharyngeal swabs tested, and males constituted the majority tested (73.4%). A total of 7737 (7.7%) individuals were SARS-CoV-2 positive by RT-PCR. The majority (i.e., 92.4%) of the RT-PCR positive individuals were asymptomatic. Testing was dominated by mass screening and travellers, and even at health facility level 91.6% of tests were from individuals without symptoms. Out of the 97,124 tests from asymptomatic individuals 7,149 (7%) were positive and of the 2,568 symptomatic individuals 588 (23%) were positive. In total, 2458 serum samples were submitted with paired naso-pharyngeal/oro-pharyngeal samples and 45% of the RT-PCR positive samples and 20% of the RT-PCR negative samples were paired with positive serum samples. Symptomatic individuals had significantly higher antibody levels than asymptomatic individuals and become RT-PCR negative on repeat testing earlier than asymptomatic individuals. Conclusions: In conclusion, the majority of SARS-CoV-2 infections identified by routine testing in Coastal Kenya were asymptomatic. This reflects the testing practice of health services in Kenya, but also implies that asymptomatic infection is very common in the population. Symptomatic infection may be less common, or it may be that individuals do not present for testing when they have symptoms.

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

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.

Using contact data to model the impact of contact tracing and physical distancing to control the SARS-CoV-2 outbreak in Kenya

Background: Across the African continent, other than South Africa, COVID-19 cases have remained relatively low. Nevertheless, in Kenya, despite early implementation of containment measures and restrictions, cases have consistently been increasing. Contact tracing forms one of the key strategies in Kenya, but may become infeasible as the caseload grows. Here we explore different contact tracing strategies by distinguishing between household and non-household contacts and how these may be combined with other nonpharmaceutical interventions. Methods: We extend a previously developed branching process model for contact tracing to include realistic contact data from Kenya. Using the contact data, we generate a synthetic population of individuals and their contacts categorised by age and household membership. We simulate the initial spread of SARS-CoV-2 through this population and look at the effectiveness of a number of nonpharmaceutical interventions with a particular focus on different contact tracing strategies and the potential effort involved in these. Results: General physical distancing and avoiding large group gatherings combined with contact tracing, where all contacts are isolated immediately, can be effective in slowing down the outbreak, but were, under our base assumptions, not enough to control it without implementing extreme stay at home policies. Under optimistic assumptions with a highly overdispersed R0 and a short delay from symptom onset to isolation, control was possible with less stringent physical distancing and by isolating household contacts only. Conclusions: Without strong physical distancing measures, controlling the spread of SARS-CoV-2 is difficult. With limited resources, physical distancing combined with the isolation of households of detected cases can form a moderately effective strategy, and control is possible under optimistic assumptions. More data are needed to understand transmission in Kenya, in particular by studying the settings that lead to larger transmission events, which may allow for more targeted responses, and collection of representative age-related contact data. © 2020. Wagner M et al.

The importance of supplementary immunisation activities to prevent measles outbreaks during the COVID-19 pandemic in Kenya.

BACKGROUND: The COVID-19 pandemic has disrupted routine measles immunisation and supplementary immunisation activities (SIAs) in most countries including Kenya. We assessed the risk of measles outbreaks during the pandemic in Kenya as a case study for the African Region. METHODS: Combining measles serological data, local contact patterns, and vaccination coverage into a cohort model, we predicted the age-adjusted population immunity in Kenya and estimated the probability of outbreaks when contact-reducing COVID-19 interventions are lifted. We considered various scenarios for reduced measles vaccination coverage from April 2020. RESULTS: In February 2020, when a scheduled SIA was postponed, population immunity was close to the herd immunity threshold and the probability of a large outbreak was 34% (8-54). As the COVID-19 contact restrictions are nearly fully eased, from December 2020, the probability of a large measles outbreak will increase to 38% (19-54), 46% (30-59), and 54% (43-64) assuming a 15%, 50%, and 100% reduction in measles vaccination coverage. By December 2021, this risk increases further to 43% (25-56), 54% (43-63), and 67% (59-72) for the same coverage scenarios respectively. However, the increased risk of a measles outbreak following the lifting of all restrictions can be overcome by conducting a SIA with ≥ 95% coverage in under-fives. CONCLUSION: While contact restrictions sufficient for SAR-CoV-2 control temporarily reduce measles transmissibility and the risk of an outbreak from a measles immunity gap, this risk rises rapidly once these restrictions are lifted. Implementing delayed SIAs will be critical for prevention of measles outbreaks given the roll-back of contact restrictions in Kenya.