Simulating Infectious Disease Dynamics

Dynamic simulation is a computational technique developed to model and analyze the behavior of various physical systems, such as industrial equipment, production chains, and logistics networks, in a virtual environment. Through the implementation of specific algorithms, simulation is able to predict the response of these systems under different operating conditions, providing valuable subsidies for performance evaluation and resource optimization.

In this work, a methodology is proposed that integrates a dynamic simulator with a metaheuristic, with the objective of determining optimal strategies for the control of epidemic diseases, considering the combination of several intervention measures. For this, a dynamic simulator was developed structured as a matrix of dimension m × n, where m represents the number of individuals in a population and n corresponds to the observable characteristics of each individual over a time interval [0, T]. This modeling allows us to follow the dynamics of the disease under study, observing, for example, the number of infected, hospitalized, and dead individuals at each instant of time t ∈ [0, T].

The temporal evolution of these variables can be controlled by different intervention measures. However, it seeks to determine, through metaheuristics, strategies for the application of these measures that are simultaneously effective and economically viable. The metaheuristics coupled to the simulator play a fundamental role in the identification of optimal or suboptimal solutions, based on cost and epidemiological impact criteria