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 urban-rural population structure are critical determinants of viral transmission in Kenya.

Graphical abstract

BackgroundThe first COVID-19 case in Kenya was confirmed on March 13 th , 2020. Here, we provide forecasts for the potential incidence rate, and magnitude, of a COVID-19 epidemic in Kenya based on the observed growth rate and age distribution of confirmed COVID-19 cases observed in China, whilst accounting for the demographic and geographic dissimilarities between China and Kenya. MethodsWe developed a modelling framework to simulate SARS-CoV-2 transmission in Kenya, KenyaCoV. KenyaCoV was used to simulate SARS-CoV-2 transmission both within, and between, different Kenyan regions and age groups. KenyaCoV was parameterized using a combination of human mobility data between the defined regions, the recent 2019 Kenyan census, and estimates of age group social interaction rates specific to Kenya. Key epidemiological characteristics such as the basic reproductive number and the agespecific rate of developing COVID-19 symptoms after infection with SARS-CoV-2, were adapted for the Kenyan setting from a combination of published estimates and analysis of the age distribution of cases observed in the Chinese outbreak. ResultsWe find that if person-to-person transmission becomes established within Kenya, identifying the role of subclinical, and therefore largely undetected, infected individuals is critical to predicting and containing a very significant epidemic. Depending on the transmission scenario our reproductive number estimates for Kenya range from 1.78 (95% CI 1.44 -2.14) to 3.46 (95% CI 2. 81-4.17). In scenarios where asymptomatic infected individuals are transmitting significantly, we expect a rapidly growing epidemic which cannot be contained only by case isolation. In these scenarios, there is potential for a very high percentage of the population becoming infected (median estimates: >80% over six months), and a significant epidemic of symptomatic COVID-19 cases. Exceptional social distancing measures can slow transmission, flattening the epidemic curve, but the risk of epidemic rebound after lifting restrictions is predicted to be high.

Policy makers in Africa need robust estimates of the current and future spread of SARS-CoV-2. Data suitable for this purpose are scant. We used national surveillance PCR test, serological survey and mobility data to develop and fit a county-specific transmission model for Kenya. We estimate that the SARS-CoV-2 pandemic peaked before the end of July 2020 in the major urban counties, with 34 - 41% of residents infected, and will peak elsewhere in the country within 2-3 months. Despite this penetration, reported severe cases and deaths are low. Our analysis suggests the COVID-19 disease burden in Kenya may be far less than initially feared. A similar scenario across sub-Saharan Africa would have implications for balancing the consequences of restrictions with those of COVID-19.