Solving a Predictive Model of Infectious Disease in Epidemiology and Epizootiology by Assigning Scores to Individuals

Abstract

Every mathematical solution to a problem corresponds to a model that more or less corresponds to reality. One of the many possibilities is the solution to epidemiological problems (processes) using the susceptible-infected-removed (SIR) model. Like any model, this model also has its advantages and benefits that allow us to close such a complicated reality as the origin and course of the epidemic into simple differential equations (DE) and through the model’s parameters modify it and compare it with reality. This is possible only in the case of a comprehensive understanding of reality in all its breadth and depth. This study focuses on another solution to the epidemiological or epizootiological processes based on the newly designed simulation, which assigns individuals a certain score in time intervals. In this case, the score from the simulation has a normal probability distribution with certain parameters N (µ, σ) in given time intervals. This score level also divides the given individuals into certain groups based on the given (selected) critical level. This determines whether individuals are with manifestations of the disease or without signs of the disease. In the study, the epidemic curve (EC) simulation results could be used to solve the current pandemic caused by COVID-19. It can be stated that the newly proposed model could be more suitable because it permanently confirms the agreement of experimental and expected frequencies.