COVID-19 mass testing and sequencing: Experiences from a laboratory in Western Kenya.

Background: The Basic Science Laboratory (BSL) of the Kenya Medical Research Institute/Walter Reed Project in Kisumu, Kenya addressed mass testing challenges posed by the emergent coronavirus disease 2019 (COVID-19) in an environment of global supply shortages. Before COVID-19, the BSL had adequate resources for disease surveillance and was therefore designated as one of the testing centres for COVID-19. Intervention: By April 2020, the BSL had developed stringent safety procedures for receiving and mass testing potentially infectious nasal specimens. To accommodate increased demand, BSL personnel worked in units: nucleic acid extraction, polymerase chain reaction, and data and quality assurance checks. The BSL adopted procedures for tracking sample integrity and minimising cross-contamination. Lessons learnt: Between May 2020 and January 2022, the BSL tested 63 542 samples, of which 5375 (8.59%) were positive for COVID-19; 1034 genomes were generated by whole genome sequencing and deposited in the Global Initiative on Sharing All Influenza Data database to aid global tracking of viral lineages. At the height of the pandemic (August and November 2020, April and August 2021 and January 2022), the BSL was testing more than 500 samples daily, compared to 150 per month prior to COVID-19. An important lesson from the COVID-19 pandemic was the discovery of untapped resilience within BSL personnel that allowed adaptability when the situation demanded. Strict safety procedures and quality management that are often difficult to maintain became routine. Recommendations: A fundamental lesson to embrace is that there is no 'one-size-fits-all' approach and adaptability is the key to success.

Genomic surveillance of SARS-COV-2 reveals diverse circulating variant lineages in Nairobi and Kiambu Counties, Kenya.

Genomic surveillance and identification of COVID-19 outbreaks are important in understanding the genetic diversity, phylogeny, and lineages of SARS-CoV-2. Genomic surveillance provides insights into circulating infections, and the robustness and design of vaccines and other infection control approaches. We sequenced 57 SARS-CoV-2 isolates from a Kenyan clinical population, of which 55 passed quality checks using the Ultrafast Sample placement on the Existing tRee (UShER) workflow. Phylo-genome-temporal analyses across two regions in Kenya (Nairobi and Kiambu County) revealed that B.1.1.7 (Alpha; n = 32, 56.1%) and B.1 (n = 9, 15.8%) were the predominant lineages, exhibiting low Ct values (5-31) suggesting high infectivity, and variant mutations across the two regions. Lineages B.1.617.2, B.1.1, A.23.1, A.2.5.1, B.1.596, A, and B.1.405 were also detected across sampling sites within target populations. The lineages and genetic isolates were traced back to China (A), Costa Rica (A.2.5.1), Europe (B.1, B.1.1, A.23.1), the USA (B.1.405, B.1.596), South Africa (B.1.617.2), and the United Kingdom (B.1.1.7), indicating multiple introduction events. This study represents one of the genomic SARS-CoV-2 epidemiology studies in the Nairobi metropolitan area, and describes the importance of continued surveillance for pandemic control.

Temporal lineage replacements and dominance of imported variants of concern during the COVID-19 pandemic in Kenya

Background Kenya’s COVID-19 epidemic was seeded early in March 2020 and did not peak until early August 2020 (wave 1), late-November 2020 (wave 2), mid-April 2021 (wave 3), late August 2021 (wave 4), and mid-January 2022 (wave 5). Methods Here, we present SARS-CoV-2 lineages associated with the five waves through analysis of 1034 genomes, which included 237 non-variants of concern and 797 variants of concern (VOC) that had increased transmissibility, disease severity or vaccine resistance. Results In total 40 lineages were identified. The early European lineages (B.1 and B.1.1) were the first to be seeded. The B.1 lineage continued to expand and remained dominant, accounting for 60% (72/120) and 57% (45/79) in waves 1 and 2 respectively. Waves three, four and five respectively were dominated by VOCs that were distributed as follows: Alpha 58.5% (166/285), Delta 92.4% (327/354), Omicron 95.4% (188/197) and Beta at 4.2% (12/284) during wave 3 and 0.3% (1/354) during wave 4. Phylogenetic analysis suggests multiple introductions of variants from outside Kenya, more so during the first, third, fourth and fifth waves, as well as subsequent lineage diversification. Conclusions The data highlights the importance of genome surveillance in determining circulating variants to aid interpretation of phenotypes such as transmissibility, virulence and/or resistance to therapeutics/vaccines. Plain language summary The SARS-CoV-2 virus that causes COVID-19 has been changing over time. We investigated the changes in the virus present in Kenya from March 2020 to January 2022. During this period there were five successive waves of infection, during which time more people were infected with the virus. The virus arrived in Kenya from other countries many different times. Identifying the changes in the virus helps us to understand how the virus changes over time and the effect this has on its ability to infect people and make them ill. Kimita et al. detail the SARS-CoV-2 lineages present in Kenya during 5 waves of the COVID-19 pandemic between March 2020 and January 2022. Analysis of 1034 genomes identified 40 lineages, with phylogenetic analysis suggesting multiple introductions of variants from outside Kenya.