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Sepsis is a life-threatening medical emergency, which is a major cause ofdeath worldwide and the second highest cause of mortality in the United States.Researching the optimal control treatment or intervention strategy on thecomprehensive sepsis system is key in reducing mortality. For this purpose,first, this paper improves a complex nonlinear sepsis model proposed in ourprevious work. Then, bifurcation analyses are conducted for each sepsissubsystem to study the model behaviors under some system parameters. Thebifurcation analysis results also further indicate the necessity of controltreatment and intervention therapy. If the sepsis system is without adding anycontrol under some parameter and initial system value settings, the system willperform persistent inflammation outcomes as time goes by. Therefore, we developour complex improved nonlinear sepsis model into a sepsis optimal controlmodel, and then use some effective biomarkers recommended in existing clinicpractices as optimization objective function to measure the development ofsepsis. Besides that, a Bayesian optimization algorithm by combining Recurrentneural network (RNN-BO algorithm) is introduced to predict the optimal controlstrategy for the studied sepsis optimal control system. The difference betweenthe RNN-BO algorithm from other optimization algorithms is that once given anynew initial system value setting (initial value is associated with the initialconditions of patients), the RNN-BO algorithm is capable of quickly predictinga corresponding time-series optimal control based on the historical optimalcontrol data for any new sepsis patient. To demonstrate the effectiveness andefficiency of the RNN-BO algorithm on solving the optimal control solution onthe complex nonlinear sepsis system, some numerical simulations are implementedby comparing with other optimization algorithms in this paper.

An Improved Mathematical Model of Sepsis: Modeling, Bifurcation Analysis, and Optimal Control Study for Complex Nonlinear Infectious Disease System