Poster Presenters

Introduction

Effective surveillance for zoonotic diseases depends on systematic capture of one health(OH) exposure data. Case investigation tools determine which exposures are documented during outbreak investigations and shape the epidemiological intelligence generated. In Nigeria, mpox surveillance relies on a national case investigation form; however, its capacity to capture One Health–relevant exposure data remains unclear. 

Aim

This study evaluated whether Nigeria’s mpox case investigation tool captured the OH exposure variables required to identify zoonotic transmission pathways.

Methods

A structured content analysis of Nigeria’s national mpox case investigation form (one standardized tool comprising six sections) was conducted using a predefined OH exposure framework. The tool was purposively selected as the standard surveillance instrument in use, and all variables across its six sections were included in the analysis. Variables were systematically extracted and mapped to five exposure domains: animal contact, wildlife interaction, animal product handling, environmental exposures, and occupational risks. Variables were coded for presence or absence within each domain. Descriptive analysis was used to assess domain representation and the analytical utility of the tool for identifying zoonotic transmission pathways.

Results

The tool comprehensively captured demographic, clinical, and laboratory information required for case confirmation. However, none of the five predefined One Health exposure domains—animal contact, wildlife interaction, animal product handling, environmental exposures, and occupational risks—were represented. Consequently, the tool generated no structured data on zoonotic exposures, limiting its capacity to support analysis of animal reservoirs, environmental drivers, or spillover pathways during outbreak investigations.

Conclusion

Absence of One Health exposure variables constrained the ability of surveillance systems to detect and analyse zoonotic transmission pathways. Strengthening case investigation tools to include standardized animal and environmental exposure variables is essential to operationalize One Health surveillance, improve outbreak investigations, and enhance epidemic preparedness.

Background

Cholera remains significant public health threat in Kenya, particularly in areas with limited access to safe water, sanitation, and hygiene (WaSH). In September 2025, cholera outbreak was detected in Narok County with spread to Migori. Nairobi reported an isolated case. We describe the epidemiological characteristics of the outbreak and interventions implemented to interrupt transmission.

Methods

We conducted retrospective analysis of the national cholera line-list for September-December 2025, calculated descriptive statistics, reviewed outbreak investigation reports, Incident Management System Team(IMST) meeting-minutes, Oral Cholera Vaccination(OCV), post-OCV coverage Survey(PCS), and Intra-Action Review(IAR) reports to identify interventions implemented, outbreak drivers, and response challenges.

Results

A total of 299 cholera cases (attack rate: 42.9/100,000 population) were reported across three counties. Narok had 82.3%(246) of cases. Males were 65% (194), 31% (73) were aged 25-44years, 29 cases were laboratory-confirmed, seven deaths occurred (CFR=2.3%), all in Narok. All 100%(12) of sampled water sources were contaminated (E.coli/coliforms). NPHEOC was activated within 24hours of notification. National cholera IMS constituted, with weekly national coordination meetings to guide response actions. Interventions included outbreak investigation, active case search, contact tracing, Cholera Treatment Centres, laboratory testing, Risk Communication and Community Engagement (RCCE), WaSH, inter-county coordination, Case-Area-Targeted-Interventions (CATI), OCV campaign, PCS and IAR. Field investigations revealed consumption of untreated water among respondents (80%), inadequate sanitation in gold-mining areas, delayed health-seeking behaviour, and low index of suspicion as drivers of transmission. The OCV campaign achieved 93.7% coverage (239,341/255,313) with PCS of 94.1% (732/778). The last case was reported on 28 December 2025. After four weeks of enhanced surveillance without new cases, the outbreak was declared over on 27 January 2026

Conclusion

A coordinated multisectoral response integrating surveillance, case management, WaSH interventions, RCCE, CATI, OCV, PCS, and IAR likely contributed to controlling the outbreak within three months. Strengthening these interventions remains essential to preventing future outbreaks.

Background: Kakamega County faces a high rabies risk, having reported the most dog bites in Kenya through KHIS (2017–2025). This investigation followed a suspected rabies death reported in Matungu Sub county, Kakamega County on 10th February 2026. Our objective was to investigate rabies outbreak in humans and animal population in selected sub counties of Kakamega County in February 2026

Methods: We conducted a case investigation on the reported case, a retrospective review of human and animal health records from January 2021 to December 2025, active case searches at both health facility and community levels, and key informant interviews. The primary focus was Matungu subcounty, with additional subcounties selected based on the burden of dog bites. Data were collected using standardized tools in the KoboToolbox and analyzed using descriptive epidemiological methods.

Results: We confirmed rabies in a three-year-old male from Matungu Sub-County who died after sustaining a scratch from a stray dog and not receiving post-exposure prophylaxis. The dog’s whereabouts were unknown. There was an identified suspected rabies death in December 2025 at the county referral hospital. Animal health records documented 77 suspected rabies cases in animals between 2022 and 2025. System gaps included limited access to historical patient data due to electronic system transitions and lack of standardized reporting tools for rabies. Although the County One Health Unit is established, coordination remains at the county level. Availability of PEP was inconsistent across facilities. Mass dog vaccination campaigns were irregular, despite a high stray dog population.

Conclusion: There were weaknesses in rabies surveillance and prevention systems in Kakamega County. Strengthening One Health coordination, improving surveillance and reporting systems, ensuring consistent availability of PEP, expanding community awareness programs, and implementing regular mass dog vaccination campaigns are critical to reducing rabies transmission and preventing future deaths.

Introduction: Influenza A (H1N1) remains a global health threat due to its zoonotic nature and potential for rapid spread. On April 7, 2025, an H1N1 outbreak was confirmed in Kisoro District, South-western Uganda. Of 38 samples tested (36 human and 2 animal), 45% (18) were confirmed positive by reverse transcriptase polymerase chain reaction at Uganda Virus Research Institute. Despite laboratory confirmation, the district lacked access to the laboratory results dispatch system, resulting in delayed receipt of test results and suboptimal response coordination.

Aim: We conducted an assessment to evaluate initial district-level laboratory capacity, identify gaps, and implement interventions to strengthen response during the H1N1 outbreak in Kisoro District, Uganda, during March–April 2025.

Methods: We applied a modified World Health Organization capacity assessment tool to assess the functionality of the district laboratory pillar and laboratory rapid response teams (RRTs). The tool assessed coordination structures, human resource capacity for sample collection and referral, laboratory logistics, infrastructure, and access to the results dispatch system. We implemented interventions through multi-stakeholder engagement and participation in district task force meetings.

Results: Six of 20 laboratories in the district served as sample collection sites. All lacked coordination structures including a laboratory pillar and RRTs. Among eight laboratory professionals at the hospital laboratory, none had documented training in sample management. Although reagent stocks were adequate to support response for one week, sample collection materials were insufficient to sustain the response. The district lacked a sample referral register, laboratory work plan, and a dedicated outbreak budget.

Conclusion: The assessment revealed gaps in laboratory coordination, workforce capacity, logistics, and sample tracking, which hinder effective outbreak response. Strengthening district laboratory pillars and RRTs is critical for timely detection, confirmation, and effective outbreak response.

Introduction: In low and middle-income countries, deaths that occur outside health facilities may remain undocumented, creating critical blind spots in health security. Kenya’s m-Dharura platform, an innovative mobile electronic Event-Based Surveillance (EBS) tool, empowers Community Health Promoters (CHPs) to report public health events in real time. This analysis aims to demonstrates how digital innovation can close mortality data gaps and support equitable health security accross Kenya. (Ndegwa et al., 2023)

Methods: The complete 2025 m-Dharura dataset (9,198 signals) was screened in Nakuru County. After excluding facility-based and non-death events, 1,468 community death records across 11 sub-counties were extracted. Age groups followed WHO-adapted categories; main causes and related factors were thematically classified from narrative text. Descriptive statistics, cross-tabulations, and geographic comparisons were performed in StataMP 17 and Excel software with no data imputation.

Results: Of 1,468 deaths, 1,158 (78.9%) had undetermined causes, adata quality limitation indicating gaps in signal verification, risk assessmnet and cause documentation. 310(21.1%) deaths were linked to preventable causes (suicides, homicides, drownings, injuries, and RTA). Males accounted for 44.8%, females 28.0%, and sex was unknown for 27.2%. Adults and the elderly accounted for the majority of deaths (n=708). Deaths occurred mainly at residences (53.2%). Njoro Sub-County recorded the highest burden (277 deaths), with first-quarter peak of 440 deaths (30%). Sub-county-level patterns revealed distinct hotspots for road traffic accidents, suicides, and drowning

Conclusion: The m-dharura digital platform successfully captured community mortality at scale, yet the proprtion of undetermined causes underscores substantial limitations in signal verification, risk assessment and cause documentation. Targeted CHP and HCWs training on signal reporting verification and risk assement, comprehenssive documentation to asertain cause, community sensitization, and sub county specific interventions (road safety in Njoro/Rongai, male-focused suicide, and drowning prevention) can ensure m-Dharura gives actionable alerts advancing Kenya’s health security through science, innovation and equity.

Background: Malaria remains a public health concern in highland epidemic-prone areas with unstable transmission driven by climatic variability. Strengthened surveillance and community engagement are essential for early detection and response to outbreaks. This study evaluated malaria trends, key transmission drivers, and the role of surveillance and community engagement in epidemic preparedness in Trans Nzoia.

Methods: A retrospective study was conducted using routine data from the Kenya Health Information System (KHIS), vector control reports, and meteorological data (2021–2025). Monthly malaria incidence was analysed in relation to long-lasting insecticidal net (LLIN) coverage, rainfall, testing rates, and case management practices. A time series regression model quantified predictor effects while adjusting for confounding factors. Testing rates and reporting completeness were included as covariates to control changes in surveillance over time, and incidence was standardized to testing volume to reduce diagnostic bias. Quartile analysis classified transmission intensity and identified high-risk periods, while sensitivity analyses assessed robustness under varying reporting completeness.

Results: Malaria incidence declined from 82.29 per 1,000 population in 2021 to 51.48 in 2022 following mass LLIN distribution. It increased to 60.57 in 2023–2024, associated with higher rainfall and reduced preventive measure use, before declining to 41.34 in 2025 alongside strengthened surveillance and community engagement. After adjusting for testing rates and reporting completeness, trends remained significant, confirming reductions were not solely due to improved testing or reporting. High transmission periods aligned with upper quartiles and increased rainfall.

Conclusion: Malaria trends are shaped by environmental factors, intervention coverage, and health system performance. Adjusting for surveillance-related confounders enhances the validity of findings. Sustained vector control, climate-informed strategies, and strengthened surveillance systems are critical for improved epidemic preparedness and sustained malaria control.

Background: The Kenya Malaria Strategy aims to reduce malaria incidence by 80% of 2023 levels by 2028. Emerging threats such as climate change, new vectors and parasites are major hindrances to achieving this goal. In January 2024, Wajir County, situated in Northern Kenya and considered a low-malaria-transmission zone, reported a >30-fold surge in malaria cases. Rainfall in Oct–Nov 2023 (2992mm) was significantly more than the same period in 2022 (110mm). An outbreak investigation was carried out to characterize cases and implement control measures.

Methods: Data of confirmed malaria cases were collected from Nov 2023 to February 2024 from 38 health facilities that reported confirmed malaria cases above the thresholds. Inpatient malaria cases were a proxy for severe malaria. Descriptive and bivariate analyses of the factors associated with severe malaria were conducted. 

Results: A total of 1083 confirmed malaria cases were detected, with an attack rate of 3.21 per 1000, up from 0.02 per 1000 the previous year. Most cases were reported in January 799 (70.3%), two months after the peak rainfall. Males were 711 (65.6%), and 936 (86.4%) were aged ≥5 years. The predominant species was Plasmodium falciparum, 967 (89.3%). Plasmodium vivax cases were 32 (3.0%), of which 28(87.5%) resided in the Northern part of the county bordering Ethiopia. Severe malaria cases were 118 (10.9%), of which 99 (83.9%) were ≥5 years, and there were two deaths (Case fatality rate 1.68%). The odds of developing severe malaria were higher in the southern lowlands (OR- 2.12, CI 1.42-3.16) than in the Northern Hilly areas. 

Conclusion: Both uncomplicated and severe malaria occurred more in the older age groups. Those in the lowlands were at increased risk of severe malaria. Plasmodium vivax detected could imply cross-border transmission. Age and geographical targeting of malaria control strategies are vital for the county. 

Background
In 2025, Kenya conducted a nationwide mapping of laboratory and surveillance capacities to assess progress since baseline assessments in 2020 and to identify priority actions for strengthening health security systems.

Methods
A cross-sectional study was conducted using a standardized self-assessment checklist aligned with IHR 2005. The study purposively selected and assessed 567 laboratories and evaluated surveillance and epidemic preparedness capacities in 169 sub-counties. Key laboratory domains assessed included workforce, diagnostic capacity for priority diseases, policy, equipment, commodities, data, quality assurance, and biosafety/biosecurity.

Results
Routine diagnostics for high-burden priority diseases (HIV, TB, malaria) were widely available (98%) across all facility tiers, reflecting sustained programmatic investments. Only 15% of facilities assessed had capacity for microbiology culture tests and only 14% for antimicrobial susceptibility testing. Diagnostics for emerging infectious threats (e.g., VHFs and priority zoonoses) were only available at national reference laboratories and a few regional, veterinary, and research laboratories. Overall, equipment functionality was high (93%), though only 57% had active service contracts. Commodity availability (63%) and quality management systems (68%) were the weakest performing laboratory domains. Higher tier (levels 5-6) and reference laboratories consistently outperformed lower tier laboratories (levels 3-4) laboratories. Counties in arid and semi-arid regions consistently had low capacity in all domains.

Preparedness for high-consequence pathogens remained limited: only 9% of sub-counties had VHF preparedness plans, 11% had trained rapid response teams, 14% maintained emergency stockpiles, and 43% of counties lacked functional Public Health Emergency Operations Centres.

Conclusion

While Kenya has laid a strong foundation for detection and surveillance that can be leveraged, investments in lower-tier laboratories, particularly in quality management systems, equipment management, and decentralization of diagnostics for emerging threats are still needed. Comprehensive preparedness plans, with targeted capacity building efforts, and strategic stockpiling will also be key for a resilient national detection and surveillance system.

Background: The spread of antimicrobial resistance (AMR) from anthropogenic activities into wildlife is a critical One Health and national health security concern. However, the baseline resistome of wild African primates remains poorly characterized at the genomic level. This study profiled the gut microbiome of wild baboons in the Amboseli ecosystem, Kenya, to quantify the prevalence and genomic context of antimicrobial resistance genes (ARGs).

Methods: We performed shotgun metagenomic sequencing on 98 fecal samples from a wild baboon population, integrating this with high-resolution multi-group GPS spatial tracking. We reconstructed Metagenome-Assembled Genomes (MAGs) and profiled ARGs using ABRicate and the CARD database across 724 high-quality MAGs (>90% completeness,

Introduction

Strengthening surveillance systems is essential for HIV epidemic control. Adolescents and young people (AYP) aged 15–24 years continue to experience gaps in HIV testing and treatment linkage despite progress toward the UNAIDS 95-95-95 targets. Nakuru County reports high burden of new HIV infections among AYP.

Aims

This evaluation aimed to identify missed opportunities in HIV detection and treatment linkage among adolescents and young people in Nakuru County, Kenya.

Methods

A mixed-methods evaluation of the HIV Testing Services surveillance system was conducted in Nakuru County. Electronic medical record data for AYP tested for HIV between January 2020 and June 2025 were analyzed. Self-administered semi-structured questionnaires for healthworkers. Surveillance system attributes were assessed using the CDC surveillance evaluation framework.

Results

A total of 49,225 AYP were tested for HIV, of whom 76.3% were female, yielding a female-to-male testing ratio of 3.2:1. Overall, 603 individuals tested HIV-positive, corresponding to a positivity rate of 1.2% (95% CI: 1.1–1.3). HIV positivity was highest among 15-year-olds (2.0%). Among males, positivity increased from 0.6% among those aged 15–19 years to 1.4% among those aged 20–24 years. First-time testers accounted for 40.4% of all tests and had higher positivity than repeat testers (1.7% vs 1.0%). Geographic hotspots were identified in Nakuru East (2.0%; PR=1.7), Njoro (2.0%; PR=1.7), and Kuresoi South (1.9%; PR=1.6). HIV positivity varied across service entry points, ranging from 2.9% in PMTCT-ANC to 0.2% in mobile outreach. Among AYP diagnosed with HIV, 35.0% (211/603) had no documented ART initiation. Surveillance system performance rated excellent for data quality (99.5%), acceptability (92%), representativeness (100%), moderate-timeliness (76%).

Conclusion

The surveillance system performed well. Gaps remained in detection and ART linkage among adolescents and young people. Targeted hotspot testing, male-focused testing strategies, and improved ART linkage documentation may strengthen detection and treatment engagement.

As schistosomiasis control programs in western Kenya achieve very low prevalence levels following rounds of MDA, there is urgent need for targeted strategies that identify and treat remaining infected individuals while avoiding overtreatment. This study evaluated the feasibility of implementing 5T strategy for Schistosoma mansoni in 2 wards with very low baseline prevalence (1–2%) in Bumula Sub-County.

5T was implemented using school-based (5–14 years) and health facility-based ( ≥5 years) platforms in South and West Bukusu Wards. Primary cases were tested using point-of-care circulating cathodic antigen (POC-CCA3) assay; positive individuals were treated and tracked to their households. Up to five household contacts (secondary cases) were tested and treated if positive. Contacts of SICs were tracked, tested (tertiary cases), and treated if positive. We documented prevalence, yield of positive cases, facilitating factors, barriers,and lessons.

A total of 1,269 participants were enrolled. Overall prevalence was 2.1% .Among 1,186 primary cases screened, 19 PICs (1.6%) were identified. Tracking PICs yielded 67 secondary cases, with 5 SICs (7.5%); tracking SICs yielded 16 tertiary cases, with 3 TICs (18.8%). The 5T strategy identified 8 additional positive cases beyond the 19 PICs, all received treatment.The school platform demonstrated superior yield compared to health facilities (15 vs. 4 PICs). All secondary and tertiary cases were located within 24 hours. Facilitating factors included effective coordination with CHPs and real-time data tracking. A limitation was lack of directly observed treatment for praziquantel and concern over POC‑CCA3 stability.

The 5T strategy is feasible, demonstrates good community acceptance, and effectively identifies additional infections in low-prevalence settings where MDA would not be justified.The school platform offers superior case detection yield.To support schistosomiasis elimination efforts in Kenya, the 5T approach should be considered for integration into routine primary healthcare services, with attention to addressing diagnostic quality control and community sensitization.

Background: Enteric diseases remain a major cause of morbidity and mortality in sub-Saharan Africa, where safe drinking water access is limited. On 1st March, 2025, Kisii County reported an outbreak of a febrile gastrointestinal illness in Tabaka Ward.

Aim: To determine the outbreak magnitude, aetiology, assess response timeliness, and guide control measures.

Methods: We conducted a descriptive outbreak investigation using a standard case definition. Active case finding and retrospective records review were performed using a structured questionnaire between 3rd  February and 8th March 2025. Clinical and water samples were analyzed using standard microbiological methods, multiplex PCR, and genome sequencing. Response timeliness was evaluated using the WHO 7-1-7 framework. Data were analyzed in Microsoft Excel 2016 and QGIS v3.24.1.

Results: We identified 173 cases (117 from health facilities and 56 community), with a median age of 15 years (IQR 8.8–31.2) and 57% were female. Amarondo village recorded the highest attack rate (21/100 persons). Among 36 interviewed community cases, 76% had consumed untreated water from the community spring. Forty-six clinical and 19 water samples were collected. Stool testing identified: Enteropathogenic Escherichia coli (n=8), Salmonella Typhi (n=5) with Norovirus, Giardia lamblia, Cryptosporidium, and Campylobacter (n=2 each). Five blood samples were positive for S. Typhi. All E. coli isolates were resistant to ampicillin, and 3 of the S. Typhi isolates were multidrug-resistant. Coliforms and E. coli were detected in 17 and 15 of the water samples, respectively. Detection and response benchmarks were achieved with a 16-day notification delay. Interventions included chlorine tablet distribution and community sensitizations. The CFR was 0%.

Conclusion: The outbreak was driven by contaminated water, characterized by multiple pathogens and compounded by antimicrobial resistance. Limited laboratory diagnostic capacity and poor documentation undermined timely notification. Routine water-quality monitoring, consistent WASH practices, stronger surveillance integration, improved diagnostics, and antimicrobial stewardship are essential to prevent future outbreaks.