Revealing the extent of the first wave of the COVID-19 pandemic in Kenya based on serological and PCR-test data [version 2;peer review: 2 approved, 1 approved with reservations]

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.

Infection patterns of endemic human coronaviruses in rural households in coastal Kenya.

Background: The natural history and transmission patterns of endemic human coronaviruses are of increased interest following the emergence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Methods: In rural Kenya 483 individuals from 47 households were followed for six months (2009-10) with nasopharyngeal swabs collected twice weekly regardless of symptoms. A total of 16,918 swabs were tested for human coronavirus (hCoV) OC43, NL63 and 229E and other respiratory viruses using polymerase chain reaction. Results: From 346 (71.6%) household members, 629 hCoV infection episodes were defined, with 36.3% being symptomatic: varying by hCoV type and decreasing with age. Symptomatic episodes (aHR=0.6 (95% CI:0.5-0.8) or those with elevated peak viral load (medium aHR=0.4 (0.3-0.6); high aHR=0.31 (0.2-0.4)) had longer viral shedding compared to their respective counterparts. Homologous reinfections were observed in 99 (19.9%) of 497 first infections. School-age children (55%) were the most common index cases with those having medium (aOR=5.3 (2.3 - 12.0)) or high (8.1 (2.9 - 22.5)) peak viral load most often generating secondary cases. Conclusion: Household coronavirus infection was common, frequently asymptomatic and mostly introduced by school-age children. Secondary transmission was influenced by viral load of index cases. Homologous-type reinfection was common. These data may be insightful for SARS-CoV-2.

COVID-19 transmission dynamics underlying epidemic waves in Kenya.

Policy decisions on COVID-19 interventions should be informed by a local, regional and national understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission. Epidemic waves may result when restrictions are lifted or poorly adhered to, variants with new phenotypic properties successfully invade, or infection spreads to susceptible subpopulations. Three COVID-19 epidemic waves have been observed in Kenya. Using a mechanistic mathematical model, we explain the first two distinct waves by differences in contact rates in high and low social-economic groups, and the third wave by the introduction of higher-transmissibility variants. Reopening schools led to a minor increase in transmission between the second and third waves. Socioeconomic and urban­rural population structure are critical determinants of viral transmission in Kenya.

Using contact data to model the impact of contact tracing and physical distancing to control the SARS-CoV-2 outbreak in Kenya [version 1;peer review: awaiting peer review]

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 non-pharmaceutical 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 non-pharmaceutical 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.