Recent discovery and spread of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), and the resulting disease associated with this virus, Coronavirus Disease 2019 (COVID-19), has brought unprecedented morbidity and mortality worldwide. ^{1- 3} Tracking the extent of the virus spread and disease severity in various populations is important in informing the local, national, and global public health response. Real time reverse transcription–polymerase chain reaction (rRT-PCR) testing has been the mainstay diagnostic test for COVID-19 surveillance. rRT-PCR is expensive and requires specialised infrastructure, equipment, and skills. These laboratory challenges compounded by global shortages of supplies and restrictions in shipping has resulted in sub-optimal implementation of rRT-PCR in low resource settings. Serologic tests that are cheaper than rRT-PCR are important in determining population level prevalence of SARS-CoV-2 infections. Infected individuals, including those with asymptomatic and mild disease, develop an immune response with detectable antibodies within two weeks of exposure ^{4, 5} and for months afterwards ⁶ allowing inferences to be made on the true extent of exposure in the population.

In Kenya, the first case of SARS-CoV-2 infection was detected on 12 ^th March 2020, and as of 30 ^th November 2020, a total of 83,316 rRT-PCR confirmed cases and 1,452 deaths (case fatality rate, 1.7%) were reported by the Ministry of Health (MoH). ^{7, 8} The national MoH data shows two major waves of increased transmission in Kenya observed prior to this serosurvey; the first wave happened between June and August 2020 and the second wave between October and November 2020. ⁹ Nevertheless, with limited testing resources, Kenya implemented a strategy to prioritize testing only symptomatic persons who presented at health facilities and met the suspect case definitions. ¹⁰ Along with the suboptimal contact tracing, the MoH's counts likely underreports cases by excluding individuals with asymptomatic and mild cases of COVID-19 who are less likely to seek healthcare. Serologic testing may offer additional surveillance insights. Previous findings from SARS-CoV-2 antibody testing of serum from Kenya's National Blood Transfusion Services by Kenya Medical Research Institute (KEMRI)-Wellcome Trust investigators correlated well with the observed increase in community transmissions. The investigators reported a marked increase in crude prevalence of SARS-CoV-2 antibodies from 5.6% in May to 13.3% by August 2020. ^{11, 12} In Nairobi County, the increase in seroprevalence was more than double in the same period; from a baseline of 8.9% in May to 21.5% in August 2020.

The distribution of SARS-CoV-2 infections is unlikely to be homogeneous across all communities and regions, and informal settlement environments such as Kibera in Nairobi may be disproportionately affected due to overcrowding, water, sanitation, and hygiene (WASH) infrastructure constraints, and socio-economic challenges that make adoption and implementation of COVID-19 public health mitigation measures difficult. A serosurvey in July 2020 in Mumbai, India found the seroprevalence among slum residents to be nearly 3.6 times that of non-slum residents. ¹³ There have been very few serosurvey data in informal settlements in Kenya. ¹⁴ This article provides findings on seroprevalence and risk factors associated with history of SARS-CoV-2 infection from a population-based seroprevalence survey in Kibera, the largest urban, densely populated, informal settlement in Nairobi, Kenya.

We report findings from a population-based seroprevalence survey in an urban informal settlement setting in Kenya aligned with WHO's UNITY seroepidemiological protocol. ¹⁸ An overall seroprevalence of 43.3% in Kibera, the largest urban, densely populated informal settlement in Nairobi, Kenya was observed. A Nairobi-wide serosurvey conducted at the same time utilizing similar methods (specimen collection and testing) reported a lower overall seroprevalence of 32.7% in the County. ¹⁷ However, the authors noted the seroprevalence in the Nairobi county-wide survey varied across populations with densely populated areas having the highest seroprevalence. The larger Kibera area (known as Kibra subcounty) had a seroprevalence of 42.8%, which corresponds with our finding of 43.3%. Though there are no other published population-based serosurveys in Kenya, estimates from convenient samples of mothers attending antenatal services at Kenyatta National Hospital located in the same administrative area and various cadres of healthcare workers from the same hospital had comparable seroprevalences of 41% and 44%, respectively. ^{12, 24} There are limited serosurveys from informal settlements beyond Kenya but one such study conducted in July 2020 in Mumbai, India found the seroprevalence among slum dwellers to be nearly 3.6 times that of non-slum residents. ¹³ Taken together, the associated challenges for residents of informal settlement to implement mitigation measures such as social distancing, wearing of face masks, and optimal hygiene practices could explain the increased transmission in these populations.

A high level of SARS-CoV-2 exposure in households was recorded with more than two-thirds (69%) of the study households having at least one seropositive member. The seropositivity within households with at least one seropositive person and two or more participants enrolled ranged from 17% to 100% with a median of 50% compared to the overall household seropositivity of 69%. The lower seroprevalence among younger household members suggests transmission outside the household may have played an important role in infection among adults. This finding aligns with observations from the Nairobi county-wide serosurvey and strengthens the argument of increased risk of infection from outside the household, especially among the working populations. ¹⁷ However, a serosurvey in Singapore showed higher seroprevalence among household contacts compared to work and other social contacts. ²⁵ Further, household age structure appeared to play a role with majority of seronegative persons in the exposed households being less than 20 years old. These findings conform to the lower incidence and prevalence of COVID-19 infection found among children in Kenya and other countries. ²⁶ Consistent with epidemiological findings of rRT-PCR confirmed SARS-CoV-2 infections, children had a lower cumulative risk of infection than adults. Lower expression of angiotensin converting enzyme 2 in children relative to adults has been considered as one hypothesis for the observed reduced risk. ²⁷ Higher prevalence among adolescents 10-19 years compared to younger children below 5 years, also conform with earlier documentation of increasing risk of infection with increasing age among children and adolescents ²⁸ which could partly be attributed to increased interaction outside households. Adherence to COVID-19 mitigation measures within the households such as hand hygiene was not assessed in this study but highly unlikely to have reduced the infections rates among younger children given the WASH challenges reported in Kibera. Schools in Kenya had been closed since the confirmation of the first case in March 2020 to the time of this serosurvey, potentially reducing young children's contact to persons living beyond their immediate neighbourhood hence exposure to infectious individuals. The schools have since reopened and a follow up survey would delineate any changes in transmission in the school going children as well as the rest of the population.

Our data show that most of the older persons were in their own business or employment potentially increasing their risk of exposure while on public transport and/or at workplaces. Adherence to mitigation measures may be suboptimal in these settings in Kenya. Although children were not going to school, in the informal settlement with limited indoor space, the anecdotal evidence point to considerable peer interactions as children played outdoors. This is a paradox to disentangle with further investigations when schools open.

Individuals from small (≤2 members) and large (≥5 persons) households had increased odds of being seropositive compared to those from medium-sized (3-4 persons) households. While this observation appears counter intuitive with respect to the role of crowding, most medium-sized households had parents and their children which dovetail well with the lower risk of infection among the young children as discussed earlier. Small-sized households consisted of mainly adults (spouses) who needed to go out for work, while for the larger households, there were more adults, suggesting overcrowding and more adults who needed to go out for work. Sharing of bed space overnight by couples could also partly explain the relatively high risk of infection in the small sized households.

This study had some limitations. First, not all members of the selected households were enrolled, and as shown in Figure 2, the probability of inclusion varied by age and sex. For instance, adult males were underrepresented as they were frequently working at the time of household visits and sampling. This would most likely lead to underestimation of the true population SARS-CoV-2 exposure given the working populations seemed to have higher seroprevalence. However, we have weighted the reported estimates by probabilities of inclusion to generate population level estimates. Second, sample size was limited and possibly inadequate for some of the stratified and regression analyses. Third, the reported seroprevalence was not adjusted for assay performance. Although the Wantai kit was verified in a CDC-supported laboratory, ¹⁷ sensitivity and specificity estimates from local or similar populations were lacking. The manufacturer's estimates are from a different population to provide meaningful interpretation. The kinetics of antibodies are not fully elucidated, and we may have missed those who were infected several months prior due to waning of detectable antibodies. It would be informative to have serial serosurveys in the seropositive participants to assess the longevity of detectable SARS-CoV-2 antibodies. Finally, seropositivity was not confirmed by a neutralisation or a secondary assay.

In densely populated urban settings where the implementation of mitigation measures – such as case identification and isolation, contact tracing and quarantine, and social distancing – remained very challenging, close to half of the individuals have had aSARS-CoV-2 infection eight months into the COVID-19 pandemic in Kenya. This highlights the importance to prioritize additional mitigation measures, including COVID-19 vaccines, in these crowded, low socioeconomic settings.

The dataset and analyses code are available at Harvard Dataverse. DOI: https://doi.org/10.7910/DVN/LWJH9N. ²⁹

This project contains the following underlying data: -

pbids_age_gender_weights.tab

Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).

wAccess to the dataset is restricted as it contains sensitive participant identifying information. Accompanying documentation is available under open access. For more detailed information beyond the metadata and documentation provided, there is a process of managed access requiring submission of a request, detailing the intended use, for consideration by our Data Governance Committee. Please contact the Data Governance Committee via this email address - gbigogo@kemricdc.org.