Use of Sentinel Surveillance Platforms for Monitoring SARS-CoV-2 Activity: Evidence From Analysis of Kenya Influenza Sentinel Surveillance Data.

BACKGROUND: Little is known about the cocirculation of influenza and SARS-CoV-2 viruses during the COVID-19 pandemic and the use of respiratory disease sentinel surveillance platforms for monitoring SARS-CoV-2 activity in sub-Saharan Africa. OBJECTIVE: We aimed to describe influenza and SARS-CoV-2 cocirculation in Kenya and how the SARS-CoV-2 data from influenza sentinel surveillance correlated with that of universal national surveillance. METHODS: From April 2020 to March 2022, we enrolled 7349 patients with severe acute respiratory illness or influenza-like illness at 8 sentinel influenza surveillance sites in Kenya and collected demographic, clinical, underlying medical condition, vaccination, and exposure information, as well as respiratory specimens, from them. Respiratory specimens were tested for influenza and SARS-CoV-2 by real-time reverse transcription polymerase chain reaction. The universal national-level SARS-CoV-2 data were also obtained from the Kenya Ministry of Health. The universal national-level SARS-CoV-2 data were collected from all health facilities nationally, border entry points, and contact tracing in Kenya. Epidemic curves and Pearson r were used to describe the correlation between SARS-CoV-2 positivity in data from the 8 influenza sentinel sites in Kenya and that of the universal national SARS-CoV-2 surveillance data. A logistic regression model was used to assess the association between influenza and SARS-CoV-2 coinfection with severe clinical illness. We defined severe clinical illness as any of oxygen saturation <90%, in-hospital death, admission to intensive care unit or high dependence unit, mechanical ventilation, or a report of any danger sign (ie, inability to drink or eat, severe vomiting, grunting, stridor, or unconsciousness in children younger than 5 years) among patients with severe acute respiratory illness. RESULTS: Of the 7349 patients from the influenza sentinel surveillance sites, 76.3% (n=5606) were younger than 5 years. We detected any influenza (A or B) in 8.7% (629/7224), SARS-CoV-2 in 10.7% (768/7199), and coinfection in 0.9% (63/7165) of samples tested. Although the number of samples tested for SARS-CoV-2 from the sentinel surveillance was only 0.2% (60 per week vs 36,000 per week) of the number tested in the universal national surveillance, SARS-CoV-2 positivity in the sentinel surveillance data significantly correlated with that of the universal national surveillance (Pearson r=0.58; P<.001). The adjusted odds ratios (aOR) of clinical severe illness among participants with coinfection were similar to those of patients with influenza only (aOR 0.91, 95% CI 0.47-1.79) and SARS-CoV-2 only (aOR 0.92, 95% CI 0.47-1.82). CONCLUSIONS: Influenza substantially cocirculated with SARS-CoV-2 in Kenya. We found a significant correlation of SARS-CoV-2 positivity in the data from 8 influenza sentinel surveillance sites with that of the universal national SARS-CoV-2 surveillance data. Our findings indicate that the influenza sentinel surveillance system can be used as a sustainable platform for monitoring respiratory pathogens of pandemic potential or public health importance.

Seroconversion and seroprevalence of TORCH infections in a pregnant women cohort study, Mombasa, Kenya, 2017-2019.

Women infected during pregnancy with TORCH (Toxoplasmosis, Other, Rubella, Cytomegalovirus, and Herpes simplex viruses) pathogens have a higher risk of adverse birth outcomes including stillbirth / miscarriage because of mother-to-child transmission. To investigate these risks in pregnant women in Kenya, we analyzed serum specimens from a pregnancy cohort study at three healthcare facilities. A sample of 481 participants was selected for TORCH pathogen antibody testing to determine seroprevalence. A random selection of 285 from the 481 participants was selected to measure seroconversion. These sera were tested using an IgG enzyme-linked immunosorbent assay against 10 TORCH pathogens. We found that the seroprevalence of all but three of the 10 TORCH pathogens at enrollment was >30%, except for Bordetella pertussis (3.8%), Treponema pallidum (11.4%), and varicella zoster virus (0.5%). Conversely, very few participants seroconverted during their pregnancy and were herpes simplex virus type 2 (n = 24, 11.2%), parvovirus B19 (n = 14, 6.2%), and rubella (n = 12, 5.1%). For birth outcomes, 88% of the participant had live births and 12% had stillbirths or miscarriage. Cytomegalovirus positivity at enrolment had a statistically significant positive association with a live birth outcome (p = 0.0394). Of the 10 TORCH pathogens tested, none had an association with adverse pregnancy outcome.

Heterogenous transmission and seroprevalence of SARS-CoV-2 in two demographically diverse populations with low vaccination uptake in Kenya, March and June 2021.

Background: SARS-CoV-2 has extensively spread in cities and rural communities, and studies are needed to quantify exposure in the population. We report seroprevalence of SARS-CoV-2 in two well-characterized populations in Kenya at two time points. These data inform the design and delivery of public health mitigation measures. Methods: Leveraging on existing population based infectious disease surveillance (PBIDS) in two demographically diverse settings, a rural site in western Kenya in Asembo, Siaya County, and an urban informal settlement in Kibera, Nairobi County, we set up a longitudinal cohort of randomly selected households with serial sampling of all consenting household members in March and June/July 2021. Both sites included 1,794 and 1,638 participants in the March and June/July 2021, respectively. Individual seroprevalence of SARS-CoV-2 antibodies was expressed as a percentage of the seropositive among the individuals tested, accounting for household clustering and weighted by the PBIDS age and sex distribution. Results: Overall weighted individual seroprevalence increased from 56.2% (95%CI: 52.1, 60.2%) in March 2021 to 63.9% (95%CI: 59.5, 68.0%) in June 2021 in Kibera. For Asembo, the seroprevalence almost doubled from 26.0% (95%CI: 22.4, 30.0%) in March 2021 to 48.7% (95%CI: 44.3, 53.2%) in July 2021. Seroprevalence was highly heterogeneous by age and geography in these populations-higher seroprevalence was observed in the urban informal settlement (compared to the rural setting), and children aged <10 years had the lowest seroprevalence in both sites. Only 1.2% and 1.6% of the study participants reported receipt of at least one dose of the COVID-19 vaccine by the second round of serosurvey-none by the first round. Conclusions: In these two populations, SARS-CoV-2 seroprevalence increased in the first 16 months of the COVID-19 pandemic in Kenya. It is important to prioritize additional mitigation measures, such as vaccine distribution, in crowded and low socioeconomic settings.

Genome Sequence of Escherichia coli Isolated from an Adult in Kibera, an Urban Informal Settlement in Nairobi, Kenya.

An Escherichia coli strain (sequence type 636) was isolated from an adult residing in an urban informal settlement in Nairobi, Kenya, and was sequenced using the Illumina MiSeq platform. The draft genome was 5,075,726 bp, with a Col(BS512) plasmid plus aph(6)-Id, blaTEM-1B, and dfrA7 genes, which encode kanamycin, ampicillin, and trimethoprim resistance proteins, respectively.

Characterizing the Countrywide Epidemic Spread of Influenza A(H1N1)pdm09 Virus in Kenya between 2009 and 2018.

The spatiotemporal patterns of spread of influenza A(H1N1)pdm09 viruses on a countrywide scale are unclear in many tropical/subtropical regions mainly because spatiotemporally representative sequence data are lacking. We isolated, sequenced, and analyzed 383 A(H1N1)pdm09 viral genomes from hospitalized patients between 2009 and 2018 from seven locations across Kenya. Using these genomes and contemporaneously sampled global sequences, we characterized the spread of the virus in Kenya over several seasons using phylodynamic methods. The transmission dynamics of A(H1N1)pdm09 virus in Kenya were characterized by (i) multiple virus introductions into Kenya over the study period, although only a few of those introductions instigated local seasonal epidemics that then established local transmission clusters, (ii) persistence of transmission clusters over several epidemic seasons across the country, (iii) seasonal fluctuations in effective reproduction number (Re) associated with lower number of infections and seasonal fluctuations in relative genetic diversity after an initial rapid increase during the early pandemic phase, which broadly corresponded to epidemic peaks in the northern and southern hemispheres, (iv) high virus genetic diversity with greater frequency of seasonal fluctuations in 2009-2011 and 2018 and low virus genetic diversity with relatively weaker seasonal fluctuations in 2012-2017, and (v) virus spread across Kenya. Considerable influenza virus diversity circulated within Kenya, including persistent viral lineages that were unique to the country, which may have been capable of dissemination to other continents through a globally migrating virus population. Further knowledge of the viral lineages that circulate within understudied low-to-middle-income tropical and subtropical regions is required to understand the full diversity and global ecology of influenza viruses in humans and to inform vaccination strategies within these regions.

Seroprevalence and risk factors of SARS-CoV-2 infection in an urban informal settlement in Nairobi, Kenya, December 2020.

Introduction: Urban informal settlements may be disproportionately affected by the COVID-19 pandemic due to overcrowding and other socioeconomic challenges that make adoption and implementation of public health mitigation measures difficult. We conducted a seroprevalence survey in the Kibera informal settlement, Nairobi, Kenya, to determine the extent of SARS-CoV-2 infection. Methods: Members of randomly selected households from an existing population-based infectious disease surveillance (PBIDS) provided blood specimens between 27 th November and 5 th December 2020. The specimens were tested for antibodies to the SARS-CoV-2 spike protein. Seroprevalence estimates were weighted by age and sex distribution of the PBIDS population and accounted for household clustering. Multivariable logistic regression was used to identify risk factors for individual seropositivity.   Results: Consent was obtained from 523 individuals in 175 households, yielding 511 serum specimens that were tested. The overall weighted seroprevalence was 43.3% (95% CI, 37.4 - 49.5%) and did not vary by sex. Of the sampled households, 122(69.7%) had at least one seropositive individual. The individual seroprevalence increased by age from 7.6% (95% CI, 2.4 - 21.3%) among children (<5 years), 32.7% (95% CI, 22.9 - 44.4%) among children 5 - 9 years, 41.8% (95% CI, 33.0 - 51.1%) for those 10-19 years, and 54.9%(46.2 - 63.3%) for adults (≥20 years). Relative to those from medium-sized households (3 and 4 individuals), participants from large (≥5 persons) households had significantly increased odds of being seropositive, aOR, 1.98(95% CI, 1.17 - 1.58), while those from small-sized households (≤2 individuals) had increased odds but not statistically significant, aOR, 2.31 (95% CI, 0.93 - 5.74).  Conclusion: In densely populated urban settings, close to half of the individuals had an infection to SARS-CoV-2 after eight months of the COVID-19 pandemic in Kenya. This highlights the importance to prioritize mitigation measures, including COVID-19 vaccine distribution, in the crowded, low socioeconomic settings.

The impact of fecal sample processing on prevalence estimates for antibiotic-resistant Escherichia coli.

Investigators often rely on studies of Escherichia coli to characterize the burden of antibiotic resistance in a clinical or community setting. To determine if prevalence estimates for antibiotic resistance are sensitive to sample handling and interpretive criteria, we collected presumptive E. coli isolates (24 or 95 per stool sample) from a community in an urban informal settlement in Kenya. Isolates were tested for susceptibility to nine antibiotics using agar breakpoint assays and results were analyzed using generalized linear mixed models. We observed a <3-fold difference between prevalence estimates based on freshly isolated bacteria when compared to isolates collected from unprocessed fecal samples or fecal slurries that had been stored at 4°C for up to 7days. No time-dependence was evident (P>0.1). Prevalence estimates did not differ for five distinct E. coli colony morphologies on MacConkey agar plates (P>0.2). Successive re-plating of samples for up to five consecutive days had little to no impact on prevalence estimates. Finally, culturing E. coli under different conditions (with 5% CO2 or micro-aerobic) did not affect estimates of prevalence. For the conditions tested in these experiments, minor modifications in sample processing protocols are unlikely to bias estimates of the prevalence of antibiotic-resistance for fecal E. coli.

Which influenza vaccine formulation should be used in Kenya? A comparison of influenza isolates from Kenya to vaccine strains, 2007-2013.

INTRODUCTION: Every year the World Health Organization (WHO) recommends which influenza virus strains should be included in a northern hemisphere (NH) and a southern hemisphere (SH) influenza vaccine. To determine the best vaccine formulation for Kenya, we compared influenza viruses collected in Kenya from April 2007 to May 2013 to WHO vaccine strains. METHODS: We collected nasopharyngeal and oropharyngeal (NP/OP) specimens from patients with respiratory illness, tested them for influenza, isolated influenza viruses from a proportion of positive specimens, tested the isolates for antigenic relatedness to vaccine strains, and determined the percentage match between circulating viruses and SH or NH influenza vaccine composition and schedule. RESULTS: During the six years, 7.336 of the 60,072 (12.2%) NP/OP specimens we collected were positive for influenza: 30,167 specimens were collected during the SH seasons and 3717 (12.3%) were positive for influenza; 2903 (78.1%) influenza A, 902 (24.2%) influenza B, and 88 (2.4%) influenza A and B positive specimens. We collected 30,131 specimens during the NH seasons and 3978 (13.2%) were positive for influenza; 3181 (80.0%) influenza A, 851 (21.4%) influenza B, and 54 (1.4%) influenza A and B positive specimens. Overall, 362/460 (78.7%) isolates from the SH seasons and 316/338 (93.5%) isolates from the NH seasons were matched to the SH and the NH vaccine strains, respectively (p<0.001). Overall, 53.6% and 46.4% SH and NH vaccines, respectively, matched circulating strains in terms of vaccine strains and timing. CONCLUSION: In six years of surveillance in Kenya, influenza circulated at nearly equal levels during the SH and the NH influenza seasons. Circulating viruses were matched to vaccine strains. The vaccine match decreased when both vaccine strains and timing were taken into consideration. Either vaccine formulation could be suitable for use in Kenya but the optimal timing for influenza vaccination needs to be determined.

Viral shedding in patients infected with pandemic influenza A (H1N1) virus in Kenya, 2009.

BACKGROUND: Understanding shedding patterns of 2009 pandemic influenza A (H1N1) (pH1N1) can inform recommendations about infection control measures. We evaluated the duration of pH1N1 virus shedding in patients in Nairobi, Kenya. METHODS: Nasopharyngeal (NP) and oropharyngeal (OP) specimens were collected from consenting laboratory-confirmed pH1N1 cases every 2 days during October 14-November 25, 2009, and tested at the Centers for Diseases Control and Prevention-Kenya by real time reverse transcriptase polymerase chain reaction (rRT-PCR). A subset of rRT-PCR-positive samples was cultured. RESULTS: Of 285 NP/OP specimens from patients with acute respiratory illness, 140 (49%) tested positive for pH1N1 by rRT-PCR; 106 (76%) patients consented and were enrolled. The median age was 6 years (Range: 4 months-41 years); only two patients, both asthmatic, received oseltamivir. The median duration of pH1N1 detection after illness onset was 8 days (95% CI: 7-10 days) for rRT-PCR and 3 days (Range: 0-13 days) for viral isolation. Viable pH1N1 virus was isolated from 132/162 (81%) of rRT-PCR-positive specimens, which included 118/125 (94%) rRT-PCR-positive specimens collected on day 0-7 after symptoms onset. Viral RNA was detectable in 18 (17%) and virus isolated in 7/18 (39%) of specimens collected from patients after all their symptoms had resolved. CONCLUSIONS: In this cohort, pH1N1 was detected by rRT-PCR for a median of 8 days. There was a strong correlation between rRT-PCR results and virus isolation in the first week of illness. In some patients, pH1N1 virus was detectable after all their symptoms had resolved.