MULTIVARIATE SPATIOTEMPORAL STOCHASTIC MODELING OF EMERGING ZOONOTIC DISEASE SPREAD

We develop a multivariate spatiotemporal stochastic framework to explain how environmental and epidemiological drivers shape the dynamics of emerging zoonotic disease spread. The research applies the Zoonotic Spatiotemporal Risk Propagation Model using the Global Zoonotic Transmission Observatory Dataset covering Ghana during the period 2020 to 2025. Environmental drivers include climate variability, wildlife human interaction, and land use and ecological change, while public health system capacity functions as a moderating factor influencing disease outcomes. Empirical analysis integrates environmental monitoring indicators, wildlife surveillance records, and epidemiological outbreak statistics to estimate how ecological pressures interact with institutional readiness to influence transmission intensity, geographic spread patterns, outbreak frequency, and infection incidence rates. Results reveal that ecological disruption and wildlife interaction significantly intensify zoonotic disease propagation, while stronger public health surveillance infrastructure reduces the magnitude of environmental transmission pressures. The model contributes a structured empirical mechanism linking environmental exposure pathways with institutional buffering capacity in emerging disease systems. The findings offer global policy insight for strengthening integrated ecological monitoring and health surveillance strategies that support early detection and containment of zoonotic outbreaks across vulnerable health systems.