Forecasting the scale of the COVID-19 epidemic in Kenya

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.

The impact of COVID-19 control measures on social contacts and transmission in Kenyan informal settlements

“…We find a 62-67% reduction in eigenvalues of contact matrices depending on the pre-COVID-19 matrix used; assuming an R 0 of 2.6, this would translate to an R 0 of between 0.5 and 0.7 at the time of data collection. By contrast, simulation estimates of the R 0 in an unmitigated COVID-19 epidemic in Kenya were between 1.78 (95% CI 1.44-2.14) and 3.46 (95% CI 2.81-4.17) [27]. The R 0 we estimate here is consistent with the slow growth of the Kenyan epidemic to-date compared to epidemics in China and Europe.…”

Response strategies for COVID-19 epidemics in African settings: a mathematical modelling study

“…Although several studies have looked at the spread of COVID-19 in African countries [34][35][36][37][38] , we are only aware of one other modelling study which considers the impact of different interventions on the spread of COVID-19 in Africa. Walker et al 39 used a similar SEIR model and predicted a near 90% reduction in cases for sub-Saharan Africa assuming a 75% reduction in contacts starting at an incidence of 0.2 deaths per 100,000 population per week and sustained over the first 250 days of an epidemic.…”

Modelling optimal vaccination strategy for SARS-CoV-2 in the UK

“…Alongside age, it is well understood that underlying health conditions are critically important in determining the likelihood of individuals to experience severe symptoms; in younger, healthier populations a relatively low symptomatic rate has been observed [25][26][27][28] and there are few severe cases. To account for heterogeneity of risk that is not attributable to age, we therefore allowed the probability of experiencing severe health outcomes (as a result of COVID-19 disease) to be dependent on underlying health conditions.…”