Using classical and genomic epidemiology, we tracked the COVID-19 pandemic in Kenya over 23 months to determine the impact of SARS-CoV-2 variants on its progression. SARS-CoV-2 surveillance and testing data were obtained from the Kenya Ministry of Health, collected daily from 306 health facilities. COVID-19-associated fatality data were also obtained from these health facilities and communities. Whole SARS-CoV-2 genome sequencing were carried out on 1241 specimens. Over the pandemic duration (March 2020–January 2022), Kenya experienced five waves characterized by attack rates (AR) of between 65.4 and 137.6 per 100,000 persons, and intra-wave case fatality ratios (CFR) averaging 3.5%, two-fold higher than the national average COVID-19 associated CFR. The first two waves that occurred before emergence of global variants of concerns (VoC) had lower AR (65.4 and 118.2 per 100,000). Waves 3, 4, and 5 that occurred during the second year were each dominated by multiple introductions each, of Alpha (74.9% genomes), Delta (98.7%), and Omicron (87.8%) VoCs, respectively. During this phase, government-imposed restrictions failed to alleviate pandemic progression, resulting in higher attack rates spread across the country. In conclusion, the emergence of Alpha, Delta, and Omicron variants was a turning point that resulted in widespread and higher SARS-CoV-2 infections across the country.

We used whole-genome sequencing of SARS-CoV-2 to identify variants circulating in the Democratic Republic of the Congo and obtain molecular information useful for diagnosis, improving treatment, and general pandemic control strategies. A total of 74 SARS-CoV-2 isolates were sequenced using Oxford Nanopore platforms. Generated reads were processed to obtain consensus genome sequences. Sequences with more than 80% genome coverage were used for variant calling, phylogenetic analysis, and classification using Pangolin lineage annotation nomenclature. Phylogenetic analysis based on Pangolin classification clustered South Kivu sequences into seven lineages (A.23.1, B.1.1.6, B.1.214, B.1.617.2, B.1.351, C.16, and P.1). The Delta (B.1.617.2) variant was the most dominant and responsible for outbreaks during the third wave. Based on the Wuhan reference genome, 289 distinct mutations were detected, including 141 missenses, 123 synonymous, and 25 insertions/deletions when our isolates were mapped to the Wuhan reference strain. Most of these point mutations were located within the coding sequences of the SARS-CoV-2 genome that includes spike, ORF1ab, ORF3, and nucleocapsid protein genes. The most common mutation was D614G (1841A>G) observed in 61 sequences, followed by L4715L (14143 C>T) found in 60 sequences. Our findings highlight multiple introductions of SARS-CoV-2 into South Kivu through different sources and subsequent circulation of variants in the province. These results emphasize the importance of timely monitoring of genetic variation and its effect on disease severity. This work set a foundation for the use of genomic surveillance as a tool for future global pandemic management and control.

Vaccination is a key control measure of COVID-19 by preventing severe effects of disease outcomes, reducing hospitalization rates and death, and increasing immunity. However, vaccination can affect the evolution and adaptation of SARS-CoV-2, largely through vaccine-induced immune pressure. Here we investigated intrahost recombination and single nucleotide variations (iSNVs) on the SARS-CoV-2 genome in non-vaccinated and vaccinated sequences from the Kenyan population to profile intrahost viral genetic evolution and adaptations driven by vaccine-induced immune pressure. We identified recombination hotspots in the S, N, and ORF1a/b genes and showed the genetic evolution landscape of SARS-CoV-2 by comparing within-wave and inter-wave recombination events from the beginning of the pandemic (June 2020) to (December 2022) in Kenya. We further reveal differential expression of recombinant RNA species between vaccinated and non-vaccinated individuals and perform an in-depth analysis of iSNVs to identify and characterize the functional properties of non-synonymous mutations found in ORF-1 a/b, S, and N genes. Lastly, we detected a minority variant in non-vaccinated patients in Kenya, with an immune escape mutation S255F of the spike gene and showed differential recombinant RNA species. Overall, this work identified unique in vivo mutations and intrahost recombination patterns in SARS-CoV-2 which could have significant implications for virus evolution, virulence, and immune escape.

Background: Wastewater and Environmental Surveillance (WES) has become a useful public health tool as an early-warning system revealing emergence/re-emergence of pathogenic diseases and spread of antimicrobial resistance (AMR). We characterized the taxonomic composition, relative abundance, and antibiotic resistance genes (ARGs) of wastewater-identified bacterial pathogens across socioeconomically and epidemiologically diverse sewerage catchments in Nairobi, Kenya—a key East African urban city representing a low- and middle-income country (LMIC). Results: Metagenomic analysis of Nairobi’s wastewater revealed distinct bacterial and antimicrobial resistance (AMR) profiles. Campylobacteraceae (52.5% ± 17.9%) and Bacteroidaceae (19.8% ± 9.9%) dominated the communities. While Arcobacter cryaerophilus was ubiquitous, Bacteroides fragilis and A. suis abundances varied by neighborhood socioeconomic status. We detected critical clinical pathogens—including Escherichia coli, Vibrio cholerae, Mycobacterium tuberculosis, and the ESKAPE species—alongside 207 distinct ARGs conferring resistance to 11 antibiotic classes. Both taxonomic and ARG compositions showed high spatial heterogeneity, with maximum variation in low-income areas. Temporal analysis captured shifting pathogen dynamics, and metagenomic abundances for Vibrio cholerae and Klebsiella pneumoniae were validated via qPCR. Conclusions: Our findings underscore the utility of WES as a scalable, non-invasive public health tool. By capturing community-level pathogen composition and AMR dynamics, WES bypasses the limitations of clinical diagnostic access, providing a vital early-warning system for underserved urban populations. To maximize its public health utility, environmental genomic signals must serve as actionable triggers for coordinated One Health responses, including targeted clinical diagnostics, localized antimicrobial stewardship reviews, proactive risk communication, and prioritized sanitation infrastructure upgrades.