Location: Zoonotic and Emerging Disease Research
Project Number: 3022-32000-021-013-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Aug 1, 2024
End Date: Jul 31, 2025
Objective:
Contribute towards the continuous surveillance of viral pathogens and antimicrobial resistant markers and enhance our understanding of the evolutionary dynamics of pathogen risks and their potential impact on public health. This sequencing technology is intended to minimize delays between sample collection and accurate assessment of pathogens.
Approach:
75% of emerging infectious diseases are zoonotic, making their detection and characterization crucial for understanding the threat they pose to both human and animal health. New low-burden approaches to pathogen monitoring like wastewater biosurveillance are allowing better interrogation of samples from many individuals that are naturally pooled. Working in collaboration across the government, research, and industry spheres, we propose a pilot program in Kenya to leverage existing bioinformatic analysis with ongoing zoonotic pathogen surveillance to better understand pathogen risk and inform animal and public health officials in East Africa and beyond.
In low and middle-income countries (LMICs), the understanding of environmental samples as potential reservoirs for pathogens and resistance genes is notably underexplored due to a scarcity of targeted research. To date, comprehensive longitudinal or cross-sectional studies in these settings remain absent. This study will address this critical gap by analyzing a collection of samples from slaughterhouses across Western Kenya as well as wastewater in Central Kenya. By employing advanced next-generation sequencing techniques, the aim is to uncover the spectrum of prevalent pathogens and antimicrobial-resistant genes within these environments. Should this snapshot reveal the presence of such agents, it would signal a clear imperative for subsequent longitudinal and cross-sectional investigations to confirm these findings and assess their variability over time. The outcomes of this study have the potential to guide governmental policy, offering data-driven recommendations for disease control and eradication strategies in the livestock sector.
