Assessment of the pathogen genomics landscape highlights disparities and challenges for effective AMR Surveillance and outbreak response in the East African community.

The East African Community (EAC) grapples with many challenges in tackling infectious disease threats and antimicrobial resistance (AMR), underscoring the importance of regional and robust pathogen genomics capacities. However, a significant disparity exists among EAC Partner States in harnessing bacterial pathogen sequencing and data analysis capabilities for effective AMR surveillance and outbreak response. This study assesses the current landscape and challenges associated with pathogen next-generation sequencing (NGS) within EAC, explicitly focusing on World Health Organization (WHO) AMR-priority pathogens. The assessment adopts a comprehensive approach, integrating a questionnaire-based survey amongst National Public Health Laboratories (NPHLs) with an analysis of publicly available metadata on bacterial pathogens isolated in the EAC countries. In addition to the heavy reliance on third-party organizations for bacterial NGS, the findings reveal a significant disparity among EAC member States in leveraging bacterial pathogen sequencing and data analysis. Approximately 97% (n = 4,462) of publicly available high-quality bacterial genome assemblies of samples collected in the EAC were processed and analyzed by external organizations, mainly in Europe and North America. Tanzania led in-country sequencing efforts, followed by Kenya and Uganda. The other EAC countries had no publicly available samples or had all their samples sequenced and analyzed outside the region. Insufficient local NGS sequencing facilities, limited bioinformatics expertise, lack of adequate computing resources, and inadequate data-sharing mechanisms are among the most pressing challenges that hinder the EAC's NPHLs from effectively leveraging pathogen genomics data. These insights emphasized the need to strengthen microbial pathogen sequencing and data analysis capabilities within the EAC to empower these laboratories to conduct pathogen sequencing and data analysis independently. Substantial investments in equipment, technology, and capacity-building initiatives are crucial for supporting regional preparedness against infectious disease outbreaks and mitigating the impact of AMR burden. In addition, collaborative efforts should be developed to narrow the gap, remedy regional imbalances, and harmonize NGS data standards. Supporting regional collaboration, strengthening in-country genomics capabilities, and investing in long-term training programs will ultimately improve pathogen data generation and foster a robust NGS-driven AMR surveillance and outbreak response in the EAC, thereby supporting global health initiatives.

Socio-economic impacts of brucellosis on livestock production and reproduction performance in Koibatek and Marigat regions, Baringo County, Kenya.

BACKGROUND: Brucellosis in Africa is caused by Brucella species transmitted through contaminated or contacts with infected animals or their carcasses. The disease reduces livestock production and reproduction performance evident by frequent episodes of abortion, still births, swollen testes, weak calves/lambs and swollen joints. However, the socio-economic impacts of these brucellosis-associated symptoms on milk, fat, meat and blood production, infertility, sale value, dowry and costs of treatment has not been evaluated extensively in developing countries. In Baringo County, Kenya, there is a continuous movement of cattle as a result of trade and grazing, which predisposes many herds to brucellosis infection. The objective of this study was to investigate the socio-economic impacts of Brucella infection on production systems for sheep, goats, cattle and camels and explore the impact of brucellosis on livestock production and reproduction performance among livestock keeping communities in Baringo County, Kenya. The study adopted a cross-sectional survey using quantitative data collection methods. RESULTS: Results demonstrated an impact on milk production in suspected brucellosis cases resulting from abortions (OR = 0.151, P < 0.0001) and swollen joints (OR = 2.881, P < 0.0001). In terms of infertility, abortion as a symptom of brucellosis (OR = 0.440, P = 0.002), still birth (OR = 0.628, P = 0.042), and weak calf or lamb (OR = 0.525, P = 0.005) had an impact on infertility. In terms of sale value, abortion (OR = 0.385, P = 0.008), weak calf/lamb (OR = 2.963, P = 0.013) had an impact on sale value. Other analyses demonstrated that for dowry, swollen testes (OR = 5.351, P = 0.032), weak calf and lambs (OR = 0.364, P = 0.019) had a likelihood of reduction of dowry value. Finally, in terms of cost of treatment, abortion (OR = 0.449, P = 0.001), still births (OR = 0.208, P = 0.015), swollen testes (OR = 0.78, P = 0.014), weak calf/lambs (OR = 0.178, P = 0.007) and swollen joints (OR = 0.217, P = 0.003) significantly increased the costs of treatments. There was no impact on fat and meat and blood production. CONCLUSION: Even though there was a huge socio-economic impact on milk production, infertility, sale value, and dowry, it was the costs of treatment that was significantly impacted on all symptoms associated with brucellosis on this community. A 'One Health' approach in tackling the brucellosis menace as a holistic approach is recommended for both humans and their livestock.

Prevalence of brucellosis in livestock keepers and domestic ruminants in Baringo County, Kenya.

Brucellosis is widely distributed in more than 170 countries around the world, where it poses a huge threat to animal husbandry and human health. Brucellosis is a worldwide re-emerging zoonotic disease that poses serious public health problems in many developing countries including Kenya. However, prevalence of brucellosis has not been determined in Baringo County, Kenya, yet there is a continuous movement of cattle resulting from trade and grazing, thus predisposing many herds to brucellosis infection. We investigated the sero-prevalence of brucellosis in humans and domestic ruminants: sheep, goats, cattle and camels among livestock keeping communities in Baringo County, Kenya. In addition, we analyzed the seropositive samples for molecular detection of Brucella species. The study adopted a cross-sectional survey using quantitative data collection methods. The diagnosis was carried out using a competitive enzyme-linked immunosorbent assay (c-ELISA) and the real-time PCR assays. The sero-prevalence of brucellosis among human blood samples was 0.6% (n = 4/640) in Baringo County. About 22.30% (n = 143/640) of animal blood samples examined tested positive for Brucella genus-specific ELISA test. Cattle had a high prevalence of 22.88% (n = 93/322) followed by camels 20.00% (n = 21/105), goats 15.48% (n = 24/155) and subsequently sheep at 8.62% (n = 5/58). Overall, 7.5% (n = 6/80) of the seropositive samples amplified with the genus-specific primers. Brucella melitensis was detected in one out of the six genus positive samples, while none amplified with the B. abortus target. Even though there was high prevalence of brucellosis among livestock in Baringo County, the highest prevalence was invariably noted in cattle, followed by camels, goats and sheep, respectively. Livestock keepers had low prevalence of brucellosis. This implies that there was low risk of transmission of brucellosis between livestock keepers and their livestock.

Characterization of COVID-19 cases in the early phase (March to July 2020) of the pandemic in Kenya.

Background: Kenya detected the first case of COVID-19 on March 13, 2020, and as of July 30, 2020, 17 975 cases with 285 deaths (case fatality rate (CFR) = 1.6%) had been reported. This study described the cases during the early phase of the pandemic to provide information for monitoring and response planning in the local context. Methods: We reviewed COVID-19 case records from isolation centres while considering national representation and the WHO sampling guideline for clinical characterization of the COVID-19 pandemic within a country. Socio-demographic, clinical, and exposure data were summarized using median and mean for continuous variables and proportions for categorical variables. We assigned exposure variables to socio-demographics, exposure, and contact data, while the clinical spectrum was assigned outcome variables and their associations were assessed. Results: A total of 2796 case records were reviewed including 2049 (73.3%) male, 852 (30.5%) aged 30-39 years, 2730 (97.6%) Kenyans, 636 (22.7%) transporters, and 743 (26.6%) residents of Nairobi City County. Up to 609 (21.8%) cases had underlying medical conditions, including hypertension (n = 285 (46.8%)), diabetes (n = 211 (34.6%)), and multiple conditions (n = 129 (21.2%)). Out of 1893 (67.7%) cases with likely sources of exposure, 601 (31.8%) were due to international travel. There were 2340 contacts listed for 577 (20.6%) cases, with 632 contacts (27.0%) being traced. The odds of developing COVID-19 symptoms were higher among case who were aged above 60 years (odds ratio (OR) = 1.99, P = 0.007) or had underlying conditions (OR = 2.73, P < 0.001) and lower among transport sector employees (OR = 0.31, P < 0.001). The odds of developing severe COVID-19 disease were higher among cases who had underlying medical conditions (OR = 1.56, P < 0.001) and lower among cases exposed through community gatherings (OR = 0.27, P < 0.001). The odds of survival of cases from COVID-19 disease were higher among transport sector employees (OR = 3.35, P = 0.004); but lower among cases who were aged ≥60 years (OR = 0.58, P = 0.034) and those with underlying conditions (OR = 0.58, P = 0.025). Conclusion: The early phase of the COVID-19 pandemic demonstrated a need to target the elderly and comorbid cases with prevention and control strategies while closely monitoring asymptomatic cases.