Poster Presenters

Cyclospora cayetanensis, is a coccidian parasite that causes protracted and relapsing gastroenteritis that may last for months. At least 54 countries have documented C. cayetanensis infections and 13 of them have recorded cyclosporiasis outbreaks. The overall C. cayetanensis prevalence in humans worldwide is 3.55%. Highest prevalence is associated with immune-compromised individuals e.g. individuals receiving immunosuppressive therapies and is characterized by diarrhea. Transmission is via drinking contaminated water or consuming fruits and vegetables exposed to contaminated water, making it a public health concern.

Treatment with trimethoprim–sulfamethoxazole (TMP–SMX) effectively cures C. cayetanensis infection, whereas ciprofloxacin is less effective than TMP–SMX, but is suitable for patients who cannot tolerate co-trimoxazole. There is therefore a need to develop alternate drugs for treatment. An infection animal model is needed for this purpose. The study involved establishing suitability of Kenyan primates as animal models of Cyclospora infections by investigating the prevalence of non-human primate Cyclospora species to establish potential models for the disease.

Stool samples were obtained from Olive baboons, Colobus and vervet monkeys and examined using standard formalin ethyl acetate concentration procedure then examined using ultra-violet fluorescence microscopy.

Vervet monkeys were most commonly infected (42% positivity) followed by Colobus monkeys (25% positivity), then baboons (9% positivity).

The study indicated that the vervet monkey has the most potential as a candidate animal model for new therapies for Cyclosporiasis in humans.

Introduction: Kenya has strengthened its health surveillance systems through systems capable of predicting/detecting disease outbreaks i.e.  Advance Warning and Response Systems (AW&RS). Given that more than 75% of infectious diseases (IDs) of public health concern are zoonotic, research has increasingly focused on animal and human health and on assessing the impact of climate variability on climate-sensitive infectious diseases (CSIDs).

Aim: To map evidence on Kenya’s AW&RS, their enablers, and barriers to detecting IDs and CSIDs.

Methods: The authors searched six databases, Google Scholar, and Kenyan university repositories up to August 28th, 2024. Two independent reviewers conducted study selection, and data were extracted from each study by one reviewer. The results were synthesised using narrative and thematic analysis.

Results: 166 of 5742 records were included. Most systems focused on human disease surveillance (82.4%), monitoring disease burden. Some combined human and animal disease surveillance (8.1%), fewer focused on environmental surveillance (4.1%). Cohort surveillance systems and IDSR were the most common (37.2%), followed by modelling (9.4%). They mainly (27.7%) utilised automated alerts, such as Short Message Service (SMS)-based and event-based. Hospital records formed the main source of data (35.1%). Over half of the studies (57.4%) addressed CSIDs, with malaria and Rift Valley fever most reported. The AW&RS used accuracy and reliability models to assess the system’s performance, and system acceptability, and timeliness, to evaluate effectiveness. Health system-level factors were the most frequent (121 enablers, 127 barriers). Key enablers included skilled personnel (13 articles), whereas inadequate finances were a major barrier (21 articles). 

Conclusion: Kenya has deployed multiple AW&RS, enhancing early detection of CSIDs and zoonotic threats; these systems remain largely human-health focused and constrained by financing and coordination challenges. A detailed evaluation of the enablers/barriers and investment in one health integration is essential to maximize effectiveness.

Visceral leishmaniasis (VL) poses a significant public health problem in Kenya, with the disease endemic  in 12 counties, predominantly in arid and semi-arid regions. The Country is among 10 countries that contribute to over 95% of the new global annual VL cases. The study highlights VL epidemiological trends, treatment outcomes, and programmatic progress towards elimination using national surveillance data from 2005-2023 reported to the WHO Global Health Observatory. A total of 13,143 VL cases were reported over the study period, with increasing case detection since 2017 attributed to improved surveillance and reporting systems. Children under 14 years accounted for 68% of cases, with a male-to-female ratio of 2:1, reflecting social and behavioural exposure patterns. Seasonal transmission peaks were observed between May and July, coinciding with high sandfly densities. Most cases were reported from Marsabit, Wajir, West Pokot, and Turkana counties. HIV-VL coinfection prevalence remained low (

Background: Antimicrobial Resistance (AMR) remained a global health threat causing an estimated 1.27 million deaths in 2019, with disproportionate impact in low-and middle-income countries. Concurrently, climate change and inadequate WASH systems drove surges in climate-sensitive diseases such as cholera across Africa. In Kenya, infectious diseases and the growing AMR burden exposed fragmentation in surveillance systems highlighting need for integrated One Health surveillance across human, animal and environmental sectors.

Problem statement: Despite robust platforms, Kenyas surveillance systems faced gaps including limited Event-Based surveillance and weak laboratory integration. Operating in parallel, these systems delayed outbreak detection, obscured AMR transmission and weakened climate-informed preparedness. The study hypothesized that leveraging exisiting digital infrastructure to establish (OH-DS )platform would improve timely detection and interoperability.

Methodology:  Study supported Division of Disease Surveillance and Response Strategic Plan (2022-2026) across four pilot counties selected based on disease burden and ecological diversity. The intervention deployed an integrated OH-DS platform linking m-Dharura with KHIS2,LIMS,KABS and EARS. A mixed-method design combined quantitative analysis of surveillance performance with qualitative assessements of usability and stakeholder experiences. Data integration used automated and standardized data exchange to enable cross-sectoral aggregation of data. The platform supports real-time reporting with Key indicators including timeliness, interoperability and generation of One Health surveillance reports. Data was analyzed using pre-post comparisons with statistical tests and thematic analysis.

Results:  45% reduction in median time from event detection to notification for cholera cases representing an improvement (p

Project Title: AI in One Health: A Multi-Threat Mitigation Strategy for Global Health Security.

Lead Author: Mwenda David Thambura

Co-authors: Susan Mahero

Keywords: Artificial Intelligence, IRT, Zoonotics, One Health Surveillance

Background: The convergence of climate change, intensive livestock production, and rapid urbanization has escalated risks of zoonotic spillovers and antimicrobial resistance (AMR). This study presents a proactive diagnostic framework aligning with Kenya’s National Public Health Security Strategic Plan-2 (2026–2030) and the International Health Regulations (IHR 2005). The research facilitates a transition in disease monitoring from reactive models to an automated, AI-driven intelligence system.

Methodology: The system integrates Infrared Thermography (IRT) and a CNN-Transformer hybrid architecture to achieve non-invasive, high-throughput screening at critical surveillance nodes, including abattoirs and border points. Utilizing high-resolution thermal sensors (

Introduction
Kenya declared  Mpox outbreak in July 2024. By end of January 2026, Nairobi City County had reported 152 confirmed cases with a case fatality rate of 1.2%. Mpox cases were reported through the Integrated Disease Surveillance and Response System (IDSR); with subsequent transmission of  the same case data into the national reporting platform: All Disease Outbreak Module (ADaM). This survey characterized Mpox cases and evaluated the system performance.

Methods:
We used US-CDC and WHO guidelines. Attributes including acceptability, timeliness, data quality and stability were assessed through stakeholder interviews. Sociodemographic and clinical data of Mpox cases were extracted from county line list. Continuous variables were summarized using means/medians; categorical data with frequencies and proportions. We compared the number of Mpox cases on IDSR with the one captured on ADaM to determine data concordance in reporting.

Results:
One hundred and one stakeholders participated in the survey. The outbreak peaked in July–September 2025 (59.2%). Of 152 confirmed cases, 59.2% were male and 64.4% aged 20–40 years. Kibra subcounty recorded the highest incidence (8.8/100,000). While 85.2% of stakeholders found reporting tools easy to use, only 10.8% reported having access to ADaM.  Case contact history was documented in only 6.6% of cases, phone numbers in 46.1% and 12.5% of the line-listed cases were recorded in the national ADaM database.  Of 101 stakeholders, 64.8% reported that investigations were initiated within 7 days and laboratory turnaround exceeded 24 hours according to  81.8% of stakeholders. Reported weaknesses included lack of dedicated funding (76.9%) and logistical failures (48.5%).

Conclusion:
Mpox surveillance in Nairobi is functional but compromised by incomplete data, delayed investigations, and inadequate resources. Under-reporting of Mpox case data into ADaM was observed. Ensuring optimal system effectiveness requires improved data quality through validation checks, completeness audits, reduced laboratory turnaround times, and dedicated budgets.

Background
Prosopis juliflora invasion severely threatens rangeland productivity, native biodiversity, and pastoral livelihoods in Isiolo County, Kenya. Previous fragmented efforts have failed, necessitating a strategic, integrated framework.

Methods
The study synthesized global evidence from invaded regions (Australia, South Africa, India, Ethiopia,) using systematic qualitative synthesis. Participatory GIS mapping involving five County Implementation Group clusters was combined with a spatial decision support tool linking invasion levels, dispersal pathways, and assets to management objectives. The Farmbetter smartphone application enabled community-based surveillance. Five community validation consultations were conducted across priority areas including Ngare Mara, Merti, Garbatulla, Lorian Swamps, and Sericho.

Results
Effective management requires three interconnected pillars. Prevention protects non-invaded dry-season grazing areas through regulated grazing, enclosures, and livestock holding grounds retaining livestock for 8–10 days to clear ingested seeds. Early Detection and Rapid Response (EDRR) using smartphone-enabled surveillance allows immediate manual uprooting or cut-stump treatment of nascent infestations before soil seed banks establish. Control of established invasions requires mechanical removal combined with active restoration—reseeding native grasses (Cenchrus ciliaris) at 5 kg per acre—and a minimum of two years follow-up weeding at 10–20 hours per acre annually to deplete the persistent seed bank. Success is quantified at over 1,380 targeted reclamation acres plus 120 km² under EDRR and prevention, with labour estimated at 640 hours per acre for dense stands (≥80% cover). Layered interventions combining uprooting, reseeding, and sustained follow-up across 200 acres at Ngaremara, alongside early-stage EDRR at Garbatulla, produced demonstrably better ecological outcomes than single-method control alone.

Conclusion
By embedding community ownership, institutionalizing the County Implementation Group, and aligning with Kenya's National Prosopis Strategy, Isiolo provides a replicable, evidence-based model for invasive species management across Kenya's ASALs. The county can transition from crisis to strategic restoration, reclaiming rangelands, enhancing biodiversity, and building climate-resilient pastoral livelihoods.

Background:

Antimicrobial Resistance (AMR) is a growing public health threat that continues to limit our ability to effectively   treat diseases caused by microorganisms, raising health care costs and mortality. Without concerted efforts, the United Nations Interagency Coordination Group on AMR projected that AMR could cause approximately 10 million deaths annually and result in huge economic losses by 2050. Acinetobacter spp. infections pose significant challenges especially in intensive care units due to extensive resistance to antibiotics.

Objective:

This study demonstrates the antimicrobial resistance profile of Acinetobacter species isolates collected from multiple diagnostic sites in Kenya.

 Methods:

 A retrospective cross-sectional study design was  adopted in which all isolates received at NMRL between 2021-2023, from inpatients in 12 AMR surveillance sites were tested. The isolates were  subjected to primary culture using differential and enriched media, identification by use of both manual and automated technique (MALDI-TOF instrument). Then finally antimicrobial susceptibility testing was performed using Vitek 2 Compact automated instrument that provided results for tier one to tier three drugs.

Results:

Out of 1179 priority pathogens samples submitted, Acinetobacter spp. were isolated from 76 samples. Acinetobacter baumannii made up 87% of all Acinetobacter spp. isolated. High resistance to Ceftriaxone (90%), Ceftazidime (75%) and ciprofloxacin (61%) was  observed. The prevalence of carbapenem resistant Acinetobacter spp. was  found at 28%. Acinetobacter spp. exhibited high sensitivity to Amikacin (82%), Colistin (90%), Minocycline and Tigecycline (88%).

 Conclusions:

Acinetobacter spp. showed varied resistance to many antibiotics especially those considered first empirical treatment options. These findings are important in informing further surveillance efforts in the country, modification of treatment guidelines for Acinetobacter spp. related infections locally and heightened infection prevention and control interventions.

Escherichia coli is a priority bacterial pathogen for antimicrobial resistance (AMR) surveillance. It is one of the most frequently isolated bacterial pathogens globally and the predominant cause of urinary tract infections (UTIs), accounting for the majority of both community-acquired and hospital-acquired cases. Certain E. coli strains have developed resistance to multiple antimicrobial agents, complicating treatment options and emphasizing the need for continuous monitoring and robust AMR surveillance programs. This study aimed to evaluate the antimicrobial susceptibility profile of E. coli isolated from urine smaples.

Between 1st January 2023 and 31st December 2023, 147 E. coli isolates from urine samples were received at the National Microbiology Reference Laboratories from AMR surveillance sentinel sites for retesting. Identification was performed using Matrix Assisted Laser Desorption Ionization Time of Flight Mass Spectrophotometry (MALDI TOF MS) and Antimicrobial Susceptibility Testing (AST) was conducted using Vitek 2 Compact Equipment.  Each isolate was tested against the recommended antibiotics as per the Kenya national AMR surveillance strategy and Clinical and Laboratory Standard Institute guidelines.

Of the 147 E. coli isolates tested, 76% were resistant to ampicillin, 65% to co-trimoxazole, and 52% to cefuroxime. Lower resistance rates were observed for gentamicin (18%) and meropenem (4%). Additionally, 28% of the isolates were identified as extended-spectrum beta-lactamase (ESBL) producers. Multi-drug resistance (MDR), defined as resistance to three or more antimicrobial classes, was observed in 38% of the isolates.

The high rates of resistance to commonly used antibiotics such as ampicillin and cefuroxime together with identification of extended-spectrum beta-lactamase (ESBL) producers and multi-drug resistance (MDR) strains, underscore the urgent need for continued monitoring and surveillance of antimicrobial resistance patterns. These results emphasize the importance of judicious antibiotic use, effective infection control measures and the development of novel therapeutic strategies to combat the growing threat of antibiotic resistance in clinical settings.

Cross-border mobility along Kenya-Uganda border pose a significant risk for infectious disease transmission due to high volume of movement via both formal and informal point of entry (POEs). The assessment objectives covered the aspects of mobility patterns, population vulnerabilities and high risk interaction points in Busia and Bungoma counties. A descritive cross-sectional study using popualtion mobility mapping approach was conducted over four days in selected border areas.

The assessment began with a participatory mapping exercise involving 21 key informants from surveillance teams, transport operators, port health services, and community leadership. This was followed by site evaluations and interviews at formal POEs including Busia One Stop Border Post, Malaba One Stop Border Post, and Lwakhakha Border Post as well as selected informal crossings and congregation points. A desk review complemented field findings. Data were analyzed using thematic analysis to identify mobility drivers, routes and risk hotspots.

Trade was identified as the primary mobility driver. Busia and Malaba OSBPs operated 24 hours with high volumes of truck and pedestrian traffic, while Lwakhakha handles approximately 900 trucks entering Kenya per day with limited preparedness capacity. A total of 41 mobility routes were identified, with only 17% linked to official POEs; the remainder comprised informal land and lake crossings that bypass routine surveillance. High-risks populations included truck drivers, traders and bodaboda riders, with overnight stay by truck drivers increasing local population density and exposure risks. Additional drivers included access to healthcare, education, religious activities and family ties. High-risk locations included markets, transport hubs, informal crossings and border health facilities often characterized by inadequate infection prevention and control measures.

The findings underscored substantial cross-border interconnectedness and highlighted the need to strengthen surveillance, IPC measures, and risk communication at informal POEs, markets, transit hubs, and healthcare facilities to mitigate potential disease spread into Kenya.

Abstract

Background: Re-emergence of Mpox (formerly monkeypox), especially the severe Clade I variant with high fatality rate, poses a major public health threat. Kenya declared its first outbreak in July 2024, underscoring the urgent need for rapid national preparedness and response capabilities.

Methods: A nationwide rapid assessment was carried out in January and February 2025, with a response rate of 41/47 (87%). Using the WHO Mpox Epidemic Readiness Checklist Google Form, eight preparedness pillars were assessed: Laboratory, Infection Prevention and Control (IPC), Case Management, Surveillance, Coordination, Risk Communication and Engagement (RCCE), Operations, and Vaccination.

Results: The level of preparedness varied across pillars. Surveillance had the highest average score (59.45%), suggesting relatively stronger functionality. Case Management (47.78%), IPC (47.06%), RCCE (48.03%), and Coordination (45.98%) showed suboptimal performance. Major gaps were noted in operations (31.04%) and laboratory capacity (30.51%). The most critical gap was observed in the vaccination pillar (6.10%), suggesting a nearly total lack of strategy and capacity.

Conclusion: While the country has a functional integrated disease surveillance system, significant systemic gaps and challenges exist. Inadequate laboratory diagnosis, operational logistics, and absence of a vaccine strategy pose challenges to effective outbreak response. Targeted investments by the Ministry of Health are urgently required to strengthen these pillars and build a robust framework capable of responding to the ongoing Mpox threat and future public health emergencies.

Keywords: Mpox, Monkey pox, Preparedness, Outbreak Response, Kenya, Vaccination Strategy, Health Systems

Introduction:

Kenya’s malaria burden remains significant, with approximately 3.5 million confirmed cases annually (Ministry of Health, 2023). Plasmodium falciparum accounts for most infections, while P. vivax has historically been rare, confined mainly to northern arid counties(Elias & Massebo, 2024). However, recent surveillance data have shown an unexpected rise in P. vivax cases in Wajir County,(AFENET, 2025). The parasite’s unique biological features, including dormant liver stages (hypnozoites) that cause relapses, pose challenges to malaria elimination efforts (WHO, 2023). This investigation aimed to confirm P. vivax infections, conduct descriptive epidemiological analysis, assess emergency preparedness and response (EPR) capacity, and strengthen diagnostic and surveillance systems in Wajir County.

Methods:

A retrospective review of 75 malaria cases was conducted. Laboratory slides were retrieved and rechecked under microscopy to assess diagnostic concordance. Demographic, temporal, and geographic data were extracted and analyzed with MS Excel. Surveillance system capacity was assessed across health facilities using a structured assessment tool evaluating key IDSR and KHIS surveillance elements.

Results:

Out of 75 malaria cases reviewed, 18% (N=14) were confirmed as Plasmodium vivax. Half of the blood slides were retrieved for microscopy rechecking, concordance rate was 100%. The majority of cases occurred among males (79%, n=11), median age of 14.5 years (IQR 13). February recorded the highest number of cases (43%, n=6). Wajir North Sub-county contributed the highest proportion of cases (71%, n=10). On EPR capacity, only 38% of facilities had systems to predict malaria epidemics, 25% provided feedback to healthcare workers, while 15% had up-to-date guidelines for malaria diagnosis and treatment. None of the respondents reported receiving regular meteorological data or weekly surveillance feedback.

Conclusion:

The emergence of P. vivax malaria in Wajir County signifies a new epidemiological trend in Kenya’s arid regions. Strengthening epidemic preparedness, data use, and quality assurance is essential for effective malaria control.