Strategic intervention policies of human-to-human viral encephalitis: a mathematical control approach

Encephalitis is an acute inflammatory disease of the brain and continues to pose a significant public health challenge, particularly in the case of viral infections capable of sustained human-to-human transmission and progression to severe clinical outcomes. Effective disease management requires intervention strategies that can reduce transmission while avoiding excessive strain on limited healthcare resources. In this study, we develop and analyze an SEITR-type compartmental model that incorporates multiple intervention measures, including prevention, early treatment, intermittent therapy, and suppressive treatment. To better capture disease severity and healthcare demand, additional compartments representing intensive care unit (ICU) admission and ventilator support are included. Numerical simulations are carried out to investigate the combined impact of these interventions on disease dynamics and associated costs. The results indicate that coordinated implementation of control measures can substantially reduce the epidemic burden, lowering the peak number of infections by approximately 85 % and cumulative cases by about 95 % compared with an uncontrolled scenario, while remaining economically feasible within the model assumptions. These findings highlight the potential benefits of integrated intervention strategies for mitigating transmission and managing healthcare capacity during encephalitis outbreaks. The proposed framework provides a quantitative basis for comparative assessment of control strategies and may serve as a decision-support tool for exploring intervention trade-offs in the context of viral encephalitis.