Abstract
This paper presents an evolutionary algorithm that simulates simplified scenarios of the diffusion of an infectious disease within a given population. The proposed evolutionary epidemic diffusion (EED) computational model has a limited number of variables and parameters, but is still able to simulate a variety of configurations that have a good adherence to real-world cases. The use of two space distances and the calculation of spatial 2-dimensional entropy are also examined. Several simulations demonstrate the feasibility of the EED for testing distinct social, logistic and economy risks. The performance of the system dynamics is assessed by several variables and indices. The global information is efficiently condensed and visualized by means of multidimensional scaling.
Highlights
The diffusion of a infectious disease within a given population during a short time period is called an epidemic
If the infection spreads to a large number of countries and to other continents it may be classified as a pandemic
These two additional repositories are allocated to the elements of the population that are found by health care system as infected or that passed away due to the action of the virus
Summary
The diffusion of a infectious disease within a given population during a short time period is called an epidemic. Matematicas.uclm.es/cemat/covid19/en/ by the Spanish Mathematics Committee, or the Global research on coronavirus disease (COVID-19) https://www.who.int/emergencies/diseases/novelcoronavirus-2019/global-research-on-novel-coronavirus-2019-ncov by the World Health Organization (WHO), just to name a few In this area of computer science, evolutionary computation provides a framework for optimization schemes inspired by biological evolution [22,23,24,25,26]. We can question if a computational scheme following the concepts of EA can overcome those problems and represent a valid alternative for modeling purposes Having these ideas in mind, this paper develops an EA for mimicking a epidemic diffusion within a population. As usual with these computational schemes several simplifications will be adopted both to speed-up the computational processing and to clarify the role of the distinct parameters and variables.
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