Implementation of a triangular probabilistic distribution for optimal parametrization of the SEIR model recovery rates with delay

Abstract

Dynamical analysis of epidemiological models received significant attention after the global disaster of the Covid-19 pandemic. Although much of the attention has focused on choosing the appropriate model to describe and interpret the epidemiological data and on predicting the spread of the disease, working on uncovering the hidden dynamics by analyzing the predictive data and studying the algebraic properties of the models such as the invariant spaces, the center manifolds, and the Lyapunov functions in the general parameter space still have the potential to make a significant contribution to our understanding of the dynamics of spread. In this paper, in order to determine the recovery rate based on characteristic model parameters, the triangular probabilistic distribution is implemented in an epidemic delay differential equation with delays. By defining generic coefficients derived from field surveys and population characteristics, we propose a way to reduce the number of model parameters that need to be obtained during model fitting in order to find an effective way to handle the cultural and physiological diversity of societies and isolate the effect of counter measures for the epidemics.