ZOONOTIC PATHOGEN SURVEILLANCE AT THE WILDLIFE–HUMAN INTERFACE

Abstract

Effective surveillance of zoonotic pathogens at the wildlife–human interface requires a One Health approach that integrates wildlife ecology, clinical medicine, and genomic bioinformatics. Approximately 60–75% of emerging infectious diseases in humans originate in animals (Grogan et al., 2014; Kelly et al., 2016). Field-based ecological methods such as targeted wildlife sampling, environmental and vector surveys, and risk modeling can identify high-risk reservoirs and hotspots (McKee et al., 2021; Bui et al., 2025). Medical surveillance (syndromic case-reporting, serology, and outbreak investigation) provides early human-warning systems (Kelly et al., 2016). Genomic bioinformatics (metagenomic sequencing, phylogenetic analysis, and big-data integration) connects field and clinical data to trace spillover and transmission (Russell et al., 2025; Goldberg et al., 2024). We review methodologies and real-world case studies – including Ebola, Nipah, influenza, and SARS-CoV-2 – to illustrate how veterinarians, ecologists, clinicians, and data scientists collaborate in zoonotic surveillance (Saéz et al., 2015; McKee et al., 2021; Bui et al., 2025; Goldberg et al., 2024). We discuss challenges such as fragmented data silos, resource limitations, and evolving pathogens, and outline future directions in global One Health surveillance, including portable sequencing and AI-driven analytics (Mukherjee et al., 2025; Russell et al., 2025). This chapter underscores the necessity of interdisciplinary collaboration to detect and mitigate zoonotic threats worldwide (Kelly et al., 2016).