STOCHASTIC BEHAVIORAL ANALYSIS OF A MATHEMATICAL MODEL FOR HOSPITAL INFECTIONS

Abstract

This study aims to propose a methodology for obtaining values for a set of parameters in epidemiological mathematical models according to a specific probability distribution by means of stochastic simulations. Using the previously selected epidemiological model, the parameters with the greatest influence on the model's outcomes are identified. Subsequently, predefined functions are employed to generate random numbers based on specific probability distributions. This approach allows for the exploration of uncertainties associated with the parameters of interest by assessing their impact on the stability and dynamics of the model variables. The proposed methodology was applied to a mathematical model describing the transmission dynamics of Acinetobacter baumannii in intensive care units, with a focus on selecting parameters related to the prevention and control of pathogen dissemination. Using known information about these parameters, values were estimated to illustrate their impact on the state variables of the model. The simulations generated various scenarios, with initial conditions unfavorable to the control of pathogen transmission, providing insights into the model’s sensitivity to different epidemiological conditions. The stochastic simulation-based methodology provides a robust tool to support decision making in the development of infection control strategies, emphasizing the importance of preventive measures for infection control in ICUs and improving the understanding of Acinetobacter baumannii transmission in hospital settings.