Tracking severe acute respiratory syndrome coronavirus 2 transmission and co-infection with other acute respiratory pathogens using a sentinel surveillance system in Rift Valley, Kenya.

BACKGROUND: The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been the most significant public health challenge in over a century. SARS-CoV-2 has infected over 765 million people worldwide, resulting in over 6.9 million deaths. This study aimed to detect community transmission of SARS-CoV-2 and monitor the co-circulation of SARS-CoV-2 with other acute respiratory pathogens in Rift Valley, Kenya. METHODS: We conducted a cross-sectional active sentinel surveillance for the SARS-CoV-2 virus among patients with acute respiratory infections at four sites in Rift Valley from January 2022 to December 2022. One thousand two hundred seventy-one patients aged between 3 years and 98 years presenting with influenza-like illness (ILI) were recruited into the study. Nasopharyngeal swab specimens from all study participants were screened using a reverse transcription-quantitative polymerase chain reaction (RT-qPCR) for SARS-CoV-2, influenza A, influenza B and respiratory syncytial virus (RSV). RESULTS: The samples that tested positive for influenza A (n = 73) and RSV (n = 12) were subtyped, while SARS-CoV-2 (n = 177) positive samples were further screened for 12 viral and seven bacterial respiratory pathogens. We had a prevalence of 13.9% for SARS-CoV-2, 5.7% for influenza A, 2% for influenza B and 1% for RSV. Influenza A-H1pdm09 and RSV B were the most dominant circulating subtypes of influenza A and RSV, respectively. The most common co-infecting pathogens were Streptococcus pneumoniae (n = 29) and Haemophilus influenzae (n = 19), accounting for 16.4% and 10.7% of all the SARS-CoV-2 positive samples. CONCLUSIONS: Augmenting syndromic testing in acute respiratory infections (ARIs) surveillance is crucial to inform evidence-based clinical and public health interventions.

Whole-genome sequence analysis reveals the circulation of multiple SARS-CoV-2 variants of concern in Nairobi and neighboring counties, Kenya between March and July 2021.

The emergence and rapid spread of SARS-CoV-2 variants of concern (VOC) have been linked to new waves of COVID-19 epidemics occurring in different regions of the world. The VOC have acquired adaptive mutations that have enhanced virus transmissibility, increased virulence, and reduced response to neutralizing antibodies. Kenya has experienced six waves of COVID-19 epidemics. In this study, we analyzed 64 genome sequences of SARS-CoV-2 strains that circulated in Nairobi and neighboring counties, Kenya between March 2021 and July 2021. Viral RNA was extracted from RT-PCR confirmed COVID-19 cases, followed by sequencing using the ARTIC network protocol and Oxford Nanopore Technologies. Analysis of the sequence data was performed using different bioinformatics methods. Our analyses revealed that during the study period, three SARS-CoV-2 variants of concern (VOC) circulated in Nairobi and nearby counties in Kenya. The Alpha (B.1.1.7) lineage predominated (62.7%), followed by Delta (B.1.617.2, 35.8%) and Beta (B.1.351, 1.5%). Notably, the Alpha (B.1.1.7) VOC were most frequent from March 2021 to May 2021, while the Delta (B.1.617.2) dominated beginning June 2021 through July 2021. Sequence comparisons revealed that all the Kenyan viruses were genetically similar to those that circulated in other regions. Although the majority of Kenyan viruses clustered together in their respective phylogenetic lineages/clades, a significant number were interspersed among foreign strains. Between March and July 2021, our study's findings indicate the prevalence of multiple lineages of SAR-CoV-2 VOC in Nairobi and nearby counties in Kenya. The data suggest that the recent increase in SARS-CoV-2 infection, particularly in Nairobi and Kenya as a whole, is attributable to the introduction and community transmission of SARS-CoV-2 VOC among the populace. In conclusion, the findings provide a snapshot of the SARS-CoV-2 variants that circulated in Kenya during the study period.

Impact of influenza A(H1N1)pdm09 virus on circulation dynamics of seasonal influenza strains in Kenya.

We describe virus variations from patients with influenza-like illness before and after the appearance of influenza A(H1N1)pdm09 in Kenya during January 2008-July 2011. A total of 11,592 nasopharyngeal swabs were collected from consenting patients. Seasonal influenza B, A/H1N1, A/H3N2, A/H5N1, and influenza A(H1N1)pdm09 viruses were detected by real-time reverse transcription-polymerase chain reaction. Of patients enrolled, 2073 (17.9%) had influenza. A total of 1,524 (73.4%) of 2,073 samples were positive for influenza A virus and 549 (26.6%) were positive for influenza B virus. Influenza B virus predominated in 2008 and seasonal A(H1N1) virus predominated in the first half of 2009. Influenza A(H1N1)pdm09 virus predominated in the second half of 2009. Influenza A/H3N2 virus predominated in 2010, and co-circulation of influenza A(H1N1)pdm09 virus and influenza B virus predominated the first half of 2011. The reduction and displacement of seasonal A(H1N1) virus was the most obvious effect of the arrival of influenza A(H1N1)pdm09 virus. The decision of the World Health Organization to replace seasonal A(H1N1) virus with the pandemic virus strain for the southern hemisphere vaccine was appropriate for Kenya.

Epidemiology of 2009 pandemic influenza A virus subtype H1N1 among Kenyans aged 2 months to 18 years, 2009-2010.

BACKGROUND: The US Army Medical Research Unit-Kenya (USAMRU-K) conducts surveillance for influenza-like illness (ILI) in Kenya. We describe the temporal and geographic progression of A(H1N1)pdm09 as it emerged in Kenya and characterize the outpatient population with A(H1N1)pdm09 infection. METHODS: We included patients with ILI aged 2 months to 18 years enrolled during June 2009-August 2010. Respiratory specimens were tested by real-time reverse-transcription polymerase chain reaction for influenza virus. Patients with A(H1N1)pdm09 infection were compared to those with seasonal influenza A virus infection and those with ILI who had no virus or a virus other than influenza virus identified (hereafter, "noninfluenza ILI"). RESULTS: Of 4251 patients with ILI, 193 had laboratory-confirmed A(H1N1)pdm09 infection. The first pandemic influenza case detected by USAMRU-K surveillance was in August 2009; peak activity nationwide occurred during October-November 2009. Patients with A(H1N1)pdm09 infection were more likely to be school-aged, compared with patients with seasonal influenza A virus infection (prevalence ratio [PR], 2.0; 95% confidence interval [CI], 1.3-3.1) or noninfluenza ILI (PR, 3.2; 95% CI, 2.4-4.3). CONCLUSIONS: USAMRU-K ILI surveillance detected the geographic and temporal distribution of pandemic influenza in Kenya. The age distribution of A(H1N1)pdm09 infections included more school-aged children, compared with seasonal influenza A virus infection and noninfluenza ILI.

Molecular characterization and phylogenetic analysis of the hemagglutinin 1 protein of human influenza A virus subtype H1N1 circulating in Kenya during 2007-2008.

BACKGROUND: Among influenza viruses, type A viruses exhibit the greatest genetic diversity, infect the widest range of host species, and cause the vast majority of cases of severe disease in humans, including cases during the great pandemics. The hemagglutinin 1 (HA1) domain of the HA protein contains the highest concentration of epitopes and, correspondingly, experiences the most intense positive selection pressure. OBJECTIVES: We sought to isolate and genetically characterize influenza A virus subtype H1N1 (A[H1N1]) circulating in Kenya during 2007-2008, using the HA1 protein. METHODS: Nasopharyngeal swab specimens were collected from patients aged ≥ 2 months who presented to 8 healthcare facilities in Kenya with influenza-like illness. We tested specimens for seasonal influenza A viruses, using real-time reverse-transcription polymerase chain reaction (RT-PCR). Viruses were subtyped using subtype-specific primers. Specimens positive for seasonal A(H1N1) were inoculated onto Madin-Darby canine kidney cells for virus isolation. Viral RNAs were extracted from isolates, and the HA1 gene was amplified by RT-PCR, followed by nucleotide sequencing. Nucleotide sequences were assembled using BioEdit and translated into amino acid codes, using DS Gene, version 1.5. Multiple sequence alignments were performed using MUSCLE, version 3.6, and phylogenetic analysis was performed using MrBayes software. RESULTS: We found that, similar to A/Brisbane/59/2007 (H1N1)-like virus, which was included in the southern hemisphere vaccine for the 2009 influenza season, all 2007 Kenyan viruses had D39N, R77K, T132V, K149R, and E277K amino acid substitutions, compared with A/Solomon Islands/3/2006 (H1N1)-like virus, a component of the southern hemisphere vaccine for the 2008 influenza season. However, the majority of 2008 viruses from Kenya also had R192K and R226Q substitutions, compared with A/Solomon Islands/3/2006 (H1N1)-like virus. These 2 changes occurred at the receptor binding site. The majority of the 2008 Kenyan isolates contained N187S, G189N, and A193T mutations, which differed from A/Brisbane/59/2007 (H1N1)-like virus. The A193T substitution is involved in binding the sialic acid receptor. Phylogenetically, the 2008 Kenyan isolates grouped into 2 clusters. The main cluster contained viruses with N187S and A193T changes; residue 187 is involved in receptor binding, whereas residue 193 is at antigenic site Sb. CONCLUSION: Overall, the major genetic variations that occurred in seasonal A(H1) viruses either affected receptor binding or altered epitopes at the immunodominant sites. These genetic variations in seasonal A(H1N1) isolated in Kenya during 2007-2008 highlight the importance of continuing surveillance and characterization of emerging drift variants of influenza virus in Africa.