ONE HEALTH-BASED ZOONOTIC RISK ASSESSMENT: SPATIOTEMPORAL MODELING OF CROSS-SPECIES DISEASE SPILLOVER IN PERI-URBAN LIVESTOCK-HUMAN-WILDLIFE INTERFACES

Abstract

Zoonotic diseases, driven by complex ecological, biological, and anthropogenic interactions, pose a growing threat to global health and biosecurity. This study presents a mixed-methods approach combining field-based qualitative assessments with quantitative modeling techniques to evaluate zoonotic spillover risks. Across twenty sampled regions, pathogen prevalence in wildlife exceeded 30% in hotspot areas, correlating strongly with zones of frequent human-wildlife contact. Genomic analysis identified wildlife viruses such as WV-9 and WV-17 exhibiting over 90% similarity to human-infecting pathogens, with confirmed receptor-binding compatibility. Host-virus interaction analysis further revealed high zoonotic potential in specific virus-host pairs. Environmental drivers—particularly deforestation, rainfall anomalies, and human encroachment—were significantly associated with increased spillover risk. Spatiotemporal models highlighted District-6 and District-18 as high-risk zones based on hotspot scores and outbreak histories. Using ensemble machine learning models, predictive risk classifications achieved over 85% confidence, identifying true-positive zoonotic events with high precision. Surveillance gaps, particularly in under-monitored peri-urban zones, were evident from coverage metrics. The integration of these findings through the One Health lens underscores the necessity for coordinated surveillance, community engagement, and policy support. The study provides a validated, replicable workflow for prioritizing emerging zoonotic threats, with broad implications for global health monitoring and outbreak prevention.