Abstract

Background: Detailed understanding on SARS-CoV-2 regional transmission networks within sub-Saharan Africa is key for guiding local public health interventions against the pandemic.

Methods: Here, we analysed 1,139 SARS-CoV-2 genomes from positive samples collected between March 2020 and February 2021 across six counties of Coastal Kenya (Mombasa, Kilifi, Taita Taveta, Kwale, Tana River and Lamu) to infer virus introductions and local transmission patterns during the first two waves of infections. Virus importations were inferred using ancestral state reconstruction and virus dispersal between counties were estimated using discrete phylogeographic analysis.

Results: During Wave 1, 23 distinct Pango lineages were detected across the six counties, while during Wave 2, 29 lineages were detected; nine of which occurred in both waves, and four seemed to be Kenya specific (B.1.530, B.1.549, B.1.596.1 and N.8). Most of the sequenced infections belonged to lineage B.1 (n=723, 63%) which predominated in both Wave 1 (73%, followed by lineages N.8 (6%) and B.1.1 (6%)) and Wave 2 (56%, followed by lineages B.1.549 (21%) and B.1.530 (5%). Over the study period, we estimated 280 SARS-CoV-2 virus importations into Coastal Kenya. Mombasa City, a vital tourist and commercial centre for the region, was a major route for virus imports, most of which occurred during Wave 1, when many COVID-19 government restrictions were still in force. In Wave 2, inter-county transmission predominated, resulting in the emergence of local transmission chains and diversity.

Conclusions: Our analysis supports moving COVID-19 control strategies in the region from a focus on international travel to strategies that will reduce local transmission.

Funding: This work was funded by The Wellcome (grant numbers; 220985, 203077/Z/16/Z, and 222574/Z/21/Z) and the National Institute for Health Research (NIHR), project references: 17/63/and 16/136/33 using UK aid from the UK Government to support global health research, The UK Foreign, Commonwealth and Development Office.

Data availability

1) Sequence data have been deposited in GISAID database under accession numbers provided in Supplement File 22) Source Data files have been provided for Figures 1-2 and 4-10.3) Source Code associated with the figures has been uploaded (Source Code File 1) and also been made available through Harvard Dataverse

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Charles N Agoti

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    For correspondence
    cnyaigoti@kemri-wellcome.org
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2160-567X
  2. Lynette Isabella Ochola-Oyier

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  3. Simon Dellicour

    Department of Microbiology, Immunology and Transplantation, University of Leuven, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  4. Khadija Said Mohammed

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  5. Arnold W Lambisia

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  6. Zaydah R de Laurent

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  7. John M Morobe

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2398-6717
  8. Maureen W Mburu

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  9. Donwilliams O Omuoyo

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3900-5354
  10. Edidah M Ongera

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  11. Leonard Ndwiga

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  12. Eric Maitha

    Ministry of Health, Nairobi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  13. Benson Kitole

    Ministry of Health, Nairobi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  14. Thani Suleiman

    Ministry of Health, Nairobi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  15. Mohamed Mwakinangu

    Ministry of Health, Nairobi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  16. John K Nyambu

    Ministry of Health, Nairobi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  17. John Otieno

    Ministry of Health, Nairobi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  18. Barke Salim

    Ministry of Health, Nairobi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  19. Jennifer Musyoki

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  20. Nickson Murunga

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  21. Edward Otieno

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8014-7306
  22. John N Kiiru

    Ministry of Health, Nairobi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  23. Kadondi Kasera

    Ministry of Health, Nairobi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  24. Patrick Amoth

    Ministry of Health, Nairobi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  25. Mercy Mwangangi

    Ministry of Health, Nairobi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  26. Rashid Aman

    Ministry of Health, Nairobi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  27. Samson Kinyanjui

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  28. George Warimwe

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  29. My Phan

    Medical Research Centre, Uganda Virus Research Institute, Entebbe, Uganda
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6905-8513
  30. Ambrose Agweyu

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  31. Matthew Cotten

    Medical Research Centre, Uganda Virus Research Institute, Entebbe, Uganda
    Competing interests
    The authors declare that no competing interests exist.
  32. Edwine Barasa

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  33. Benjamin Tsofa

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1000-1771
  34. D James Nokes

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  35. Philip Bejon

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  36. George Githinji

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9640-7371

Funding

National Institute for Health Research (17/63/82)

  • D James Nokes

National Institute for Health Research (16/136/33)

  • Charles N Agoti
  • Samson Kinyanjui
  • George Warimwe
  • D James Nokes
  • George Githinji

Wellcome Trust (220985)

  • D James Nokes
  • George Githinji

Wellcome Trust (203077/Z/16/Z)

  • Edwine Barasa
  • Benjamin Tsofa
  • Philip Bejon

Wellcome Trust (220977/Z/20/Z)

  • My Phan
  • Matthew Cotten

Medical Research Council (NC_PC_19060)

  • My Phan
  • Matthew Cotten

H2020 European Research Council (n{degree sign}874850)

  • Simon Dellicour

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: Samples analysed here were collected under the Ministry of Health protocols as part of the national COVID-19 public health response. The whole genome sequencing study protocol was reviewed and approved by the Scientific and Ethics Review Committee (SERU) at Kenya Medical Research Institute (KEMRI), Nairobi, Kenya (SERU protocol #4035). Individual patient consent was not required by the committee for the use of these samples for studies of genomic epidemiology to inform public health response.

Reviewing Editor

  1. Mary Kate Grabowski, Johns Hopkins University, United States

Publication history

  1. Received: June 27, 2021
  2. Accepted: June 10, 2022
  3. Accepted Manuscript published: June 14, 2022 (version 1)

Copyright

© 2022, Agoti et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Charles N Agoti
  2. Lynette Isabella Ochola-Oyier
  3. Simon Dellicour
  4. Khadija Said Mohammed
  5. Arnold W Lambisia
  6. Zaydah R de Laurent
  7. John M Morobe
  8. Maureen W Mburu
  9. Donwilliams O Omuoyo
  10. Edidah M Ongera
  11. Leonard Ndwiga
  12. Eric Maitha
  13. Benson Kitole
  14. Thani Suleiman
  15. Mohamed Mwakinangu
  16. John K Nyambu
  17. John Otieno
  18. Barke Salim
  19. Jennifer Musyoki
  20. Nickson Murunga
  21. Edward Otieno
  22. John N Kiiru
  23. Kadondi Kasera
  24. Patrick Amoth
  25. Mercy Mwangangi
  26. Rashid Aman
  27. Samson Kinyanjui
  28. George Warimwe
  29. My Phan
  30. Ambrose Agweyu
  31. Matthew Cotten
  32. Edwine Barasa
  33. Benjamin Tsofa
  34. D James Nokes
  35. Philip Bejon
  36. George Githinji
(2022)
Transmission networks of SARS-CoV-2 in Coastal Kenya during the first two waves: a retrospective genomic study
eLife 11:e71703.
https://doi.org/10.7554/eLife.71703
  1. Further reading

Further reading

    1. Epidemiology and Global Health
    Shaun Truelove et al.
    Research Article Updated

    In Spring 2021, the highly transmissible SARS-CoV-2 Delta variant began to cause increases in cases, hospitalizations, and deaths in parts of the United States. At the time, with slowed vaccination uptake, this novel variant was expected to increase the risk of pandemic resurgence in the US in summer and fall 2021. As part of the COVID-19 Scenario Modeling Hub, an ensemble of nine mechanistic models produced 6-month scenario projections for July–December 2021 for the United States. These projections estimated substantial resurgences of COVID-19 across the US resulting from the more transmissible Delta variant, projected to occur across most of the US, coinciding with school and business reopening. The scenarios revealed that reaching higher vaccine coverage in July–December 2021 reduced the size and duration of the projected resurgence substantially, with the expected impacts was largely concentrated in a subset of states with lower vaccination coverage. Despite accurate projection of COVID-19 surges occurring and timing, the magnitude was substantially underestimated 2021 by the models compared with the of the reported cases, hospitalizations, and deaths occurring during July–December, highlighting the continued challenges to predict the evolving COVID-19 pandemic. Vaccination uptake remains critical to limiting transmission and disease, particularly in states with lower vaccination coverage. Higher vaccination goals at the onset of the surge of the new variant were estimated to avert over 1.5 million cases and 21,000 deaths, although may have had even greater impacts, considering the underestimated resurgence magnitude from the model.

    1. Epidemiology and Global Health
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    Research Article

    Background: Type 2 diabetes (T2D) accounts for ~90% of all cases of diabetes, resulting in an estimated 6.7 million deaths in 2021, according to the International Diabetes Federation (IDF). Early detection of patients with high risk of developing T2D can reduce the incidence of the disease through a change in lifestyle, diet, or medication. Since populations of lower socio-demographic status are more susceptible to T2D and might have limited resources or access to sophisticated computational resources, there is a need for accurate yet accessible prediction models.

    Methods: In this study, we analyzed data from 44,709 non-diabetic U.K. Biobank participants aged 40-69, predicting the risk of T2D onset within a selected timeframe (mean of 7.3 years with a standard deviation of 2.3 years). We started with 798 features that we identified as potential predictors for T2D onset. We first analyzed the data using gradient boosting decision trees, survival analysis, and logistic regression methods. We devised one non-laboratory model accessible to the general population and one more precise yet simple model that utilizes laboratory tests. We simplified both models to an accessible scorecard form, tested the models on normoglycemic and prediabetes sub cohorts, and compared the results to the results of the general cohort. We established the non-laboratory model using the following covariates: sex, age, weight, height, waist size, hip circumference, waist-to-hip Ratio (WHR), and Body-Mass Index (BMI). For the laboratory model, we used age and sex together with four common blood tests: HDL (high-density lipoprotein), gamma-glutamyl transferase, glycated hemoglobin, and triglycerides. As an external validation dataset, we used the electronic medical record database of Clalit Health Services.

    Results: The non-laboratory scorecard model achieved an Area Under the Receiver Operating Curve (auROC) of 0.81 (0.77-0.84 95% Confidence Interval (CI)) and an odds ratio (OR) between the upper and fifth prevalence deciles of 17.2 (5-66 95% CI). Using this model, we classified three risk groups, a group with 1% (0.8-1%), 5% (3-6%), and the third group with a 9% (7-12%) risk of developing T2D. We further analyzed the contribution of the laboratory-based model and devised a blood-test model based on age, sex and the four common blood tests noted above. In this scorecard model, we included age, sex, glycated hemoglobin (HbA1c%), gamma glutamyl-transferase, triglycerides, and HDL cholesterol. Using this model, we achieved an auROC of 0.87 (0.85-0.90 95% CI) and a deciles' OR of x48 (12-109 95% CI). Using this model, we classified the cohort into four risk groups with the following risks: 0.5% (0.4%-7%); 3% (2-4%); 10% (8-12%) and a high-risk group of 23% (10-37%) of developing T2D. When applying the blood tests model using the external validation cohort (Clalit), we achieved an auROC of 0.75 (0.74-0.75 95% CI). We analyzed several additional comprehensive models, which included genotyping data and other environmental factors. We found that these models did not provide cost-efficient benefits over the four blood test model. The commonly used German Diabetes Risk Score (GDRS) and Finnish Diabetes Risk Score (FINDRISC) models, trained using our data, achieved an auROC of 0.73 (0.69-0.76) and 0.66 (0.62-0.70), respectively, inferior to the results achieved by the four blood test model and by the Anthropometry models.

    Conclusions: The four blood tests and anthropometric models outperformed the commonly used non-laboratory models, the FINDRISC and the GDRS. We suggest that our models be used as tools for decision-makers to assess populations at elevated T2D risk and thus improve medical strategies. These models might also provide a personal catalyst for changing lifestyle, diet, or medication modifications to lower the risk of T2D onset.

    Funding: No Funders. The funders had no role in study design, data collection, interpretation, or the decision to submit the work for publication.