A new computational modelling for prediction of COVID-19 population and to approximate epidemic evolution of the disease

Abstract

Infectious diseases are growing at a fast rate. The early prediction and monitoring of the progress of such diseases, such as the recent outbreak of COVID-19, are fundamental to and infection control and rapid recovery. This paper presents (i) a new computational program based on a make blobs generator designed to predict the number of daily COVID-19 cases over a period of 30 days and (ii) a deterministic compartmental SEIQHRV model based on a system of ordinary differential equations. The objective of both the computational and mathematical models is to describe the dynamics of COVID-19 over time to understand the influence of specific parameters on its spread, while also predicting the approximate epidemic evolution of the disease. Furthermore, we evaluate and validate the new computational model by comparing our simulation results with the mathematical model, treating the latter as a reference model. Numerical simulations of the proposed models are applied to a randomly selected group of individuals. The results show that the curves obtained by the SEIQHRV mathematical model and those obtained by each scenario of the proposed computational model are approximately similar.