<![CDATA[Background: Monkeypox is a zoonotic infectious disease transmitted between humans and animal reservoirs that poses a growing public health and biosecurity concern. Its transmission dynamics involve both human-to-human interactions and cross-species transmission, which may contribute to sustained outbreaks. Mathematical modeling provides a systematic framework for analyzing these dynamics and supporting strategic intervention planning. Aims: This study aims to develop and analyze a deterministic compartmental model describing monkeypox transmission dynamics between human and rodent populations and to evaluate the impact of key epidemiological parameters on disease spread. Methods: A system of nonlinear differential equations is formulated to represent the transmission process. The human population is classified into seven compartments , while the rodent population is divided into four compartments . The basic reproduction number is derived using the next-generation matrix approach. Equilibrium analysis is conducted to determine the stability of disease-free and endemic states. Sensitivity analysis and numerical simulations are performed to assess the influence of model parameters. Results: The analysis shows that the disease-free equilibrium is locally stable when , indicating that the infection will die out, whereas endemic transmission persists when . Sensitivity analysis reveals that transmission parameters, particularly and , have the strongest influence on . Numerical simulations demonstrate that reducing transmission rates and increasing intervention-related parameters significantly decrease the number of infected individuals and the epidemic peak. Control measures targeting rodent populations also contribute to reducing sustained transmission. Conclusion: The proposed model emphasizes the critical role of integrating human health interventions with zoonotic reservoir control in mitigating monkeypox outbreaks. The findings provide a mathematical basis for supporting public health policy and strengthening national defense preparedness against emerging infectious diseases. Future research may incorporate optimal control strategies, spatial heterogeneity, and real outbreak data calibration to enhance model applicability and predictive performance.]]>
