Forecasting the scale of the COVID-19 epidemic in Kenya

  1. Samuel P. C. Brand
  2. Rabia Aziza
  3. Ivy K. Kombe
  4. Charles N. Agoti
  5. Joe Hilton
  6. Kat S. Rock
  7. Andrea Parisi
  8. D. James Nokes
  9. Matt J. Keeling
  10. Edwine W. Barasa

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Abstract

Background

The 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.

Methods

We 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 age-specific 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.

Results

We 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.

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    SciScore for 10.1101/2020.04.09.20059865: (What is this?)

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    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    As with all modelling studies, there are limitations in our modelling structure that go beyond uncertainty in parameterization. In particular, it is possible that controlling a COVID-19 epidemic in Kenya will require measures which can break transmission from household to household. However, an explicit household structure is not part of the KenyaCoV modelling framework. We replicate the effect of household structure in our modelling of social distancing using a combination of social context dependent age-structured mixing matrices and an effective decline in contact rate as individuals reduce contacts among the general population in favour of repeated contacts with a relatively small group of people within their own household. Whilst this is less realistic than a full, individual-based stochastic model, see for example Ferguson et al (Ferguson et al. 2005), the benefit of using KenyaCoV is that, in the event of a substantial COVID-19 epidemic in Kenya, the model is sufficiently performant that incorporating new information into forecasting could occur in near real-time. As mentioned above, it is critical that estimates of the symptomatic rate, clinical fraction, and rate of severe disease are derived from Kenyan case data rather than simply extrapolated from other settings. Such Kenyan-specific estimates of COVID-19, augmented by more recent data on Kenyan mobility, will allow us to better understand the link between transmission predictions (as given by KenyaCoV), and the o...

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    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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  2. Version published to 10.1101/2020.04.09.20059865v2 on medRxiv
  3. Version published to 10.1101/2020.04.09.20059865v1 on medRxiv