Study of distribution criteria for vaccines with limited doses - an approach based on complex networks
SIR, complex networks, epidemiology, genetic algorithms, centrality
The interest in the dynamics and understanding of the characteristics of infectious diseases spread dates back more than two centuries. Several perspectives can be considered in these studies, where there is a special appreciation for immunization. The assertive choice of groups of individuals to be immunized can directly impact the dynamics of contamination, both in protecting parts of the population and in minimizing the speed of contagion. Such a task is important and challenging in epidemic scenarios with reduced number of doses available, as it involves numerous combinations of choices.
The objective of this work is to propose an improvement in a methodology for choosing individuals to be immunized based on a genetic algorithm, which uses the SIR epidemiological model to evaluate the different sets proposed. Its effectiveness will be verified through a comparative study with criteria that use metrics of centrality in networks and a random choice of individuals. The results obtained by experiments that simulate epidemics with vaccination by each of the criteria will be evaluated from the perspectives that consider the number of individuals affected, the largest number of infected at the same time and the estimated contamination rate. The results obtained indicate that the systematic choice of individuals is a correct decision for this type of problem and that the results obtained by the optimization methodology are equivalent to the results delivered by the main centrality metrics used in complex networks.