Transmission networks of SARS-CoV-2 in Coastal Kenya during the first two waves: a retrospective genomic study
- Kenya Medical Research Institute-Wellcome Trust Research Programme, Kenya
- University of Leuven, Belgium
- Ministry of Health, Kenya
- Uganda Virus Research Institute, Uganda
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
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Replication Data for: Genomic surveillance reveals the spread patterns of SARS-CoV-2 in coastal Kenya during the first two wavesHarvard Dataverse, V3, UNF:6:RL6Vg7q0JyS7YoCkjhHe1A== [fileUNF].
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Genomic epidemiology of SARS-CoV-2 in coastal Kenya (March - July 2020)Github; sars-cov-2-early-phase-manuscript.
Article and author information
Author details
Donwilliams O Omuoyo
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.
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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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
Further reading
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- Epidemiology and Global Health
Background: The role of circulating metabolites on child development is understudied. We investigated associations between children's serum metabolome and early childhood development (ECD).
Methods: Untargeted metabolomics was performed on serum samples of 5,004 children aged 6-59 months, a subset of participants from the Brazilian National Survey on Child Nutrition (ENANI-2019). ECD was assessed using the Survey of Well-being of Young Children's milestones questionnaire. The graded response model was used to estimate developmental age. Developmental quotient (DQ) was calculated as the developmental age divided by chronological age. Partial least square regression selected metabolites with a variable importance projection ≥ 1. The interaction between significant metabolites and the child's age was tested.
Results: Twenty-eight top-ranked metabolites were included in linear regression models adjusted for the child's nutritional status, diet quality, and infant age. Cresol sulfate (β = -0.07; adjusted-p < 0.001), hippuric acid (β = -0.06; adjusted-p < 0.001), phenylacetylglutamine (β = -0.06; adjusted-p < 0.001), and trimethylamine-N-oxide (β = -0.05; adjusted-p = 0.002) showed inverse associations with DQ. We observed opposite directions in the association of DQ for creatinine (for children aged -1 SD: β = -0.05; p =0.01; +1 SD: β = 0.05; p =0.02) and methylhistidine (-1 SD: β = - 0.04; p =0.04; +1 SD: β = 0.04; p =0.03).
Conclusion: Serum biomarkers, including dietary and microbial-derived metabolites involved in the gut-brain axis, may potentially be used to track children at risk for developmental delays.
Funding: Supported by the Brazilian Ministry of Health and the Brazilian National Research Council.
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- Epidemiology and Global Health
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